{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VIBg-77CTFNv"
   },
   "source": [
    "# 利用thchs30为例建立一个语音识别系统\n",
    "\n",
    "\n",
    "- 数据处理\n",
    "- 搭建模型\n",
    "    - DFCNN\n",
    "\n",
    "论文地址：http://www.infocomm-journal.com/dxkx/CN/article/downloadArticleFile.do?attachType=PDF&id=166970"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f-rnXHFdYddT"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MOtaxs9iTFNw"
   },
   "source": [
    "## 1. 特征提取\n",
    "\n",
    "input为输入音频数据，需要转化为频谱图数据，然后通过cnn处理图片的能力进行识别。\n",
    "\n",
    "**1. 读取音频文件**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "id": "12mkEVxXTFNx",
    "outputId": "63d33a3a-2513-4d31-aa54-ff5ae0835112"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(157000,)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.io.wavfile as wav\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# 随意搞个音频做实验\n",
    "filepath = 'test.wav'\n",
    "\n",
    "fs, wavsignal = wav.read(filepath)\n",
    "print(type(wavsignal))\n",
    "print(wavsignal.shape)\n",
    "plt.plot(wavsignal)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RQNfK86uTFN2"
   },
   "source": [
    "**2. 构造汉明窗**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "id": "ZtdkDTRhTFN3",
    "outputId": "ed59b566-bc83-42ab-ced2-884712219c33"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3deVyVZf7/8dcHOOwIgmgqyKaIlktG7uaSLTaV0zqtWqatzkzNtE/TNDXTr5pp0XJqrGaa9sVpsU1TU1s1ITdAEcQNUAEVVBQQuH5/cOzLGCrqOec65/B5Ph4+4pxzy/32Bt5dXOe+r1uMMSillPJ9AbYDKKWUcg0tdKWU8hNa6Eop5Se00JVSyk9ooSullJ8IsrXjDh06mOTkZFu7V0opn5SdnV1hjIlv6TVrhZ6cnExWVpat3SullE8SkU2He02nXJRSyk9ooSullJ/QQldKKT+hha6UUn5CC10ppfzEUQtdRP4lImUiknOY10VEpotIoYisEpEBro+plFLqaFozQn8FOPcIr48Dejj/3Ag8f+KxlFJKHaujnodujPlKRJKPsMl44FXTtA7vEhGJEZHOxpitLsqolNvUNzSyvryajTuqKd9TS9X+AxhjEBFiwh10jAolOS6c1PhIAgPEdlyljsgVFxZ1BbY0e1zsfO5nhS4iN9I0iqdbt24u2LVSx8YYw+qSKhauLWfxujJyS3dTW9941L8X6gigT9doRqbHMzqjI707t0NEC155F49eKWqMmQnMBMjMzNQ7ayiP2bG3lnezinkvawtFFdWIQN+EGCYMSeLkLtGkxUfSsV0IMeEOAkRoaDRU7jtA2Z4aCsv2klOymx827uDvX6zj71+sI71TJJdnJnLpaQnEhAfb/ucpBbim0EuAxGaPE5zPKWVdaeV+Zn5VxNvLNlNzoJHTk9tz88g0zuzVkbjIkMP+PUcgnBQdyEnRofRNiOFi51v9ZXtqmJe3nfeyivnLp2t4et46rh6cxOThKXRsF+qhf5VSLXNFoc8GporI28AgoErnz5Vt1bX1PL9oPTO/LqKx0XDRqV25aWQq3TtGndDn7RgVytWDkrh6UBJrtu7mn4vX89LXRbz2/SZuHZXGlDNSCXUEuuhfodSxkaPdU1RE3gJGAR2A7cCfAAeAMeYFaZpIfI6mM2H2AdcbY4666lZmZqbRxbmUO8zN3cYfP8yhbE8tv+zfhTvP6UlC+3C37W9jRTWPz1nL5znb6BoTxqMX92FkeouL4Sl1wkQk2xiT2eJrtm4SrYWuXK1yXx0PfpTL7JWl9Orcjr9edAoDurX32P6XFO3ggQ9zKCzby+WZCfzx/N5EhTo8tn/VNhyp0K0tn6uUK63cUsmtb/xI2Z4a7hibzq2j03AEevZC6MGpcXzy6+FMW1DAPxevZ9nGXTx/zQAyTmrn0Ryq7dJL/5VPM8bw2pJNXPbC9wDMunkovx3bw+NlflCoI5B7zs3g7RuHUF1bzy9nfMus7GIrWVTbo4WufNaBhkbue381f/wwh6Hdm0bH/RJjbMcCYGBKLJ/8Zjj9E2O4872V/PnjXBoa9Uxd5V465aJ8UnVtPbe9+SOL8suZOro7vzsrnQAvu5KzY1Qor98wiL9+toZ/f7uRbVU1PP2r/noWjHIbLXTlc8r31DLplWXkllbx6EV9uGqQ9151HBQYwJ8uOJmuMWH85dM1VOxdyosTMvViJOUWOuWifErZ7hqumPk9hWV7eXFCpleXeXOTR6Ty3FWnsnJLFVe9uJRd1XW2Iyk/pIWufEbZ7hqufHEJW6tqeOX60zmzVyfbkY7J+X278OLETArL93L1S1rqyvW00JVPaF7m/77udAalxtmOdFxGpsfz4gQtdeUeWujK61XtP8C1L//g82V+UPNSv+6VZeyrq7cdSfkJLXTl1WoONDDl1SyKKvYy89pMny/zg0amxzPjqgGsLm66IOpAw9GX8FXqaLTQlddqaDTc/vYKftiwkycv78/wHh1sR3Kps3p34tGL+rAov5x7/7saW8twKP+hpy0qr/XIJ3nMyd3GH8/vzYX9utiO4xZXDOxG2Z5anpq3js7Rodx5Tk/bkZQP00JXXunNpZt55buNTB6ewg3DU2zHcatfj+lOaeV+nltYSI9OkYzv39V2JOWjdMpFeZ0lRTt48KMcRqbHc995vWzHcTsR4eHxpzAwOZa7Z61i5ZZK25GUj9JCV15ly8593PJ6Nt3iwpl+5alt5sbMwUEBPH/NADpEhnDja1ls311jO5LyQVroymvUHGjgpteyaWg0vDzxdKLD2tZa4nGRIbw0MZM9NfXc8nq2nvmijpkWuvIaD3+SR97W3TxzRX9SOkTYjmNFr87tePySvvy4uZLHP19rO47yMVroyit8tKKEN5du5qaRqYzJ8K1L+l3tgn5dmDAkiZe+2cCcnG224ygfooWurFtfvpf7319NZlJ77jxbT9sD+MMvetE3IZq7Zq1k8459tuMoH6GFrqyqOdDAbW/8SIgjkGevOtXanYa8TUhQIDOuGoAAU9/SK0lV6+hPj7LqL5/msXbbHp66vB+do8Nsx/EqibHhPH5JX1YVVzF9QYHtOMoHaKEraxbll/H6ks1MGZHCqJ4dbcfxSuP6dObS0xKYsbCQrI07bcdRXk4LXVmxq7qOu2etomenKH6v8+ZH9KcLetO1fRh3vLuCPTUHbMdRXkwLXXmcMYYHPsxh1746nvpVP73H5lFEhTp4+vL+lOzaz58/zrMdR3kxLXTlcR+tKOXT1Vu5fWw6J3eJth3HJ2Qmx3Lb6O7Myi7m89VbbcdRXkoLXXnU1qr9/PGjHE5Las/NI9Nsx/EpvzmzB326RvPHj3L0TkeqRVroymOMMTzwQQ71DYanLu/XZtZpcRVHYACPX9KXyn0HePgTnXpRP6eFrjzm41VbWbC2jN+fnU5SXNu8tP9E9e7SjltHpfHB8hIWri2zHUd5GS105RG7quv48+xc+iVEc/0w/17f3N1uG9OdHh0juf+D1XrWi/ofWujKIx75JI+q/Qd47JK+OtVygkKCAnni0r5s313DY7qAl2pGC1253eJ15by/vIRbRqXRq3M723H8wqnd2jNpWApvLN3M0qIdtuMoL6GFrtxqX10997+/mrT4CKaO6W47jl/5/dk9SWgfxgMf5lBXr2u9KC105WbPfllISeV+HrukLyFBegGRK4UFB/LnC0+moGwvL3+zwXYc5QW00JXbFJbt5aWvi7j0tAROT461HccvndmrE2f17sT0BQUU79Jldtu6VhW6iJwrIvkiUigi97bwejcRWSgiy0VklYic5/qoypcYY3hodi6hjkDuHZdhO45fe+jCkwF0WQB19EIXkUBgBjAO6A1cKSK9D9nsAeBdY8ypwBXAP1wdVPmWz1Zv45vCCu46pycdIkNsx/FrXWPC+O3YHszL2878vO224yiLWjNCHwgUGmOKjDF1wNvA+EO2McDB0xeigVLXRVS+prq2nkc+yePkLu24elCS7ThtwqRhKfToGMmfZueyv67BdhxlSWsKvSuwpdnjYudzzT0EXCMixcBnwK9b+kQicqOIZIlIVnl5+XHEVb5g+pcFbNtdw8PjT9Fzzj0kOCiAR355CiWV+5n5VZHtOMoSV70peiXwijEmATgPeE1Efva5jTEzjTGZxpjM+Ph4F+1aeZPCsj28/PUGLs9M4LSk9rbjtCmDU+P4RZ/OPL+4kNLK/bbjKAtaU+glQGKzxwnO55q7AXgXwBjzPRAKdHBFQOU7mt4IzSM8OJB7ztU3Qm24d1wGxqBXkLZRrSn0ZUAPEUkRkWCa3vScfcg2m4EzAUSkF02FrnMqbcyCNWV8U1jBHWelE6dvhFqRGBvOTWekMntlqd6yrg06aqEbY+qBqcBcYA1NZ7PkisjDInKhc7PfA1NEZCXwFnCdMca4K7TyPnX1jTz62RpS4yO4ZrC+EWrTzaPSOKldKH/+OI/GRv0xbEuCWrORMeYzmt7sbP7cg80+zgOGuTaa8iWvL9lEUUU1L0/MxBGo16vZFB4cxL3jMrj9nRXM+rGYyzMTj/6XlF/Qnzx1wir31TFtQQHDu3dgTEZH23EUML5/FwZ0i+GJOfm6xG4booWuTtgz8wvYU3OAB87vhYiepugNRIQ/XXAyFXtrmbFwve04ykO00NUJWV++l9eXbOJXpyeScZIujetN+iXGcPGArvzr2w2U6GmMbYIWujoh/++zNYQ6AvndWT1tR1EtuPPspq/Lk3PzLSdRnqCFro7bd4UVzF9Txq2j04iP0tMUvVGXmDAmDUvhgxUl5JRU2Y6j3EwLXR0XYwyPzVlLl+hQJuk9Qr3aLaPSiA5z8PgcvdjI32mhq+Py2eptrCqu4o6z0gl16I0rvFl0mINfj+nB1wUVfLVOr/fzZ1ro6pgdaGjkb3PXkt4pkosHJNiOo1rhmsHdSIwN4/99vpYGvdjIb2mhq2P2zrItbNyxj7vPydDVFH1ESFAgd52TwZqtu/lw+aFLMSl/oYWujsm+unqmLSjg9OT2nNlLLyLyJef36UzfhGie/CKfmgO6Zro/0kJXx+Rf32ygfE8t947L0IuIfExAgHDfuF6UVtXw6vcbbcdRbqCFrlptZ3UdLywu4qzenTgtSW/67IuGpMVxRno8zy9ar0sC+CEtdNVqMxYWsq+unrvP0YuIfNldZ/dk174DvPzNBttRlItpoatWKd61j9e+38SlpyXQo1OU7TjqBPRJiGbcKSfx0tcb2FldZzuOciEtdNUqT88rQARuH5tuO4pygd+dlc6+unpeWKwLd/kTLXR1VIVle/lgeTHXDk6iS0yY7TjKBXp0iuKiUxP4z3cb2VZVYzuOchEtdHVU0xYUEOoI5OZRabajKBe6fWwPGo1h+pcFtqMoF9FCV0e0dttuPl5ZyvXDkumg9wn1K4mx4Vw5sBvvLtvCxopq23GUC2ihqyN6et46okKCmDIi1XYU5QZTR3cnKFB4Zv4621GUC2ihq8NaXVzF3NztTB6RSkx4sO04yg06tgvluqEpfLSylLXbdtuOo06QFro6rKfm5RMT7mDS8GTbUZQb3TwylciQIJ76Qkfpvk4LXbUoe9NOFuaXc9MZaUSFOmzHUW4UEx7MDcNT+CJvu94Ew8dpoasWPfnFOjpEBjNxaJLtKMoDJg1PoV1oEM/M1zNefJkWuvqZ79ZX8N36HdwyqjvhwUG24ygPaBfqYPKIVOav2c7qYh2l+yotdPU/jDE89cU6TmoXytWDutmOozzo+mHJRIc59IwXH6aFrv7HVwUVZG3axW1juuut5dqYqFAHU0aksGBtGSu3VNqOo46DFrr6iTGGp+ato2tMGL/KTLQdR1kwcWgyMeE6SvdVWujqJ4vXlbNySyVTx3QnOEi/NdqiqFAHN56RysL8cpZv3mU7jjpG+lOrgKbR+bQFBXSNCeMSvfFzmzZxSDKxEcF6xosP0kJXAHxTWMHyzZXcMipNR+dtXERIEDeekcrideVkb9JRui/Rn1zVNDqfX0Dn6FAuy9TRuYIJQ5KIiwjWuXQfo4Wu+H79DrI27eLWUWmEBOmZLQrCg4O4aWQqXxdUkLVxp+04qpW00BXPLCigU7sQLtMzW1Qz1wxOokNkME/rKN1naKG3cd+v38EPG3Zyy8g0Pe9c/Y/w4CBuHpnGt4VN3yPK+7Wq0EXkXBHJF5FCEbn3MNtcLiJ5IpIrIm+6NqZyl+kLCoiPCuGKgXpVqPq5qwcl0SEyhGkLdJTuC45a6CISCMwAxgG9gStFpPch2/QA7gOGGWNOBm53Q1blYj9s2Mn3RTu4WUfn6jDCggO5eWQq3xbuYJnOpXu91ozQBwKFxpgiY0wd8DYw/pBtpgAzjDG7AIwxZa6Nqdxh+oICOkSGcJWOztUR/DRK1/PSvV5rCr0rsKXZ42Lnc82lA+ki8q2ILBGRc1v6RCJyo4hkiUhWeXn58SVWLpG9aSffFFZw0xmphAXr6Fwd3sFR+jeFesaLt3PVm6JBQA9gFHAl8KKIxBy6kTFmpjEm0xiTGR8f76Jdq+MxbUEhcRHBXD1YR+fq6JpG6cFMW6CjdG/WmkIvAZqfz5bgfK65YmC2MeaAMWYDsI6mgldeaPnmXXy1rpwpZ6TqeueqVcKCA7npjDS+Lqgge5OO0r1Vawp9GdBDRFJEJBi4Aph9yDYf0jQ6R0Q60DQFU+TCnMqFpi0ooH24g2sH692IVOtdPbib8+pRHaV7q6MWujGmHpgKzAXWAO8aY3JF5GERudC52Vxgh4jkAQuBu4wxO9wVWh2/lVsqWZRfzuQRqUSE6OhctV7zq0d1jRfvJMYYKzvOzMw0WVlZVvbdlt3wyjKyN+/im3vGEKmFro7Rvrp6Rjy+kJO7RvPqpIG247RJIpJtjMls6TW9UrQNySmpYsHaMm4YlqJlro5LeHDTSoxfrSvnR10v3etoobch0xYU0C40iInDkm1HUT7s2iFJxEYE63npXkgLvY3ILa1iXt52Jg1PoV2ow3Yc5cMOjtIXr9O7GnkbLfQ24tkFhUSFBHH9sBTbUZQfuHawc5Su56V7FS30NmDN1t3Myd3G9cOSiQ7T0bk6cREhQUwZkcoivfeoV9FCbwOe+7KQyJAgJg3X0blynQlDkmgf7tBRuhfRQvdz67bv4bOcrUwcmkRMeLDtOMqPRIQEMeWMplH6ii2VtuMotND93vQFBYQ7Apk8PNV2FOWHJgxJJibcwTS9q5FX0EL3Y4Vle/h09VYmDE2mfYSOzpXrRTrn0hfml7NSR+nWaaH7sWe/LCQ0KJDJOneu3GjiUOcoXefSrdNC91Pry/fy8cpSrh2SRFxkiO04yo8dHKV/ubaMVcU6SrdJC91PzfiykOCgAKaM0Llz5X4ThiQRHebQq0ct00L3QxsrqvlwRQnXDEoiPkpH58r9okIdTBmRwoK1ZawurrIdp83SQvdDzy0sxBEYwI0jdXSuPGfi0KYL16Yt0DNebNFC9zObd+zjg+UlXDWoGx2jQm3HUW1IVKiDycNTmL+mjJwSHaXboIXuZ2YsLCQwQLh5ZJrtKKoNmjgsmXahQXpXI0u00P3Ilp37+O+PxVx5eiKd2unoXHleu1AHk0ekMn/Ndh2lW6CF7kf+sWg9ASLcPEpH58qe65yjdD0v3fO00P1ESeV+ZmVv4fLTE+gcHWY7jmrD2oU6uGF4KvPydJTuaVrofuL5RYUA3DKqu+UkSjWN0qNCg5iuo3SP0kL3A1ur9vPusmIuy0yka4yOzpV90WEObhiewhd528kt1VG6p2ih+4EXFq2n0Rhu0TNblBe5fliKjtI9TAvdx23fXcNby7Zw6WkJJMaG246j1E+iwxxMGpbC3Nzt5JXuth2nTdBC93EvLF5PQ6PhVp07V15o0rAUokJ0lO4pWug+rGx3DW8u3czFp3alW5yOzpX3iQ53cP3wFObkbmPNVh2lu5sWug+b+VUR9Y2GqWN0dK681w06SvcYLXQfVb6nlteXbmJ8/y4kxUXYjqPUYUWHO7h+WDKf52xj7TYdpbuTFrqPeunrIurqG5k6WkfnyvtNGp5CpI7S3U4L3QeV76nl1e83cWG/LqTGR9qOo9RRxYQHc/2wZD5bvY38bXtsx/FbWug+6IXF66mtb+A3Z/awHUWpVrtBR+lup4XuY7bvruH1JZu4eECCjs6VT4kJD+a6ocl8lrNVR+luooXuY/6xsJCGRsNvxujoXPmeG4anEO4IZPqXOkp3By10H1JSuZ+3ftjCZZkJet658kntI4K5blgyn63eyrrtOkp3NS10HzJjYdOKilN1dK582OThqU2jdJ1Ld7lWFbqInCsi+SJSKCL3HmG7S0TEiEim6yIqaLob0bvLtnDFQF1RUfm29hHBTByazKert1Kgo3SXOmqhi0ggMAMYB/QGrhSR3i1sFwX8Fljq6pAKpi8oICBAdM0W5Rcmj3CO0r8stB3Fr7RmhD4QKDTGFBlj6oC3gfEtbPcI8DhQ48J8CthQUc37y0u4ZlASJ0XrvUKV74uNCGbC0GQ+WVVKYZmO0l2lNYXeFdjS7HGx87mfiMgAINEY8+mRPpGI3CgiWSKSVV5efsxh26rpCwpwBAo3j0q1HUUpl5kyIpUwRyDTF+go3VVO+E1REQkAngJ+f7RtjTEzjTGZxpjM+Pj4E911m1BYtocPV5QwcUgyHaN0dK78R2xEMBOGJPPxqlIKy/bajuMXWlPoJUBis8cJzucOigJOARaJyEZgMDBb3xh1jWfmFxDuCOQmvRuR8kNTRqQQ5gjkWT0v3SVaU+jLgB4ikiIiwcAVwOyDLxpjqowxHYwxycaYZGAJcKExJsstiduQ3NIqPlm1leuGJRMbEWw7jlIuFxcZwrVDkvh4pY7SXeGohW6MqQemAnOBNcC7xphcEXlYRC50d8C27G9z84kOc3DjGTo6V/7rxhGphATpeemu0Ko5dGPMZ8aYdGNMmjHmr87nHjTGzG5h21E6Oj9xS4p2sCi/nFtHpREd5rAdRym3iYsM4fphycxeWUpuaZXtOD5NrxT1QsYYHp+zlk7tQpg4NNl2HKXc7qaRTQOXJ+bk247i07TQvdC8vO0s31zJ7WPTCXUE2o6jlNtFhzm4bXQai9eV8/36Hbbj+CwtdC/T0Gj429x8UjtEcNlpCbbjKOUxE4Yk0zk6lMfmrMUYYzuOT9JC9zIfLC+hoGwvd57Tk6BA/fKotiPUEcgdY9NZuaWSubnbbMfxSdoYXqS2voGn562jT9doxp1yku04SnncxQO60r1jJE/Mzae+odF2HJ+jhe5FXl+ymZLK/dxzbgYiYjuOUh4XFBjAXef0pKi8mveyi23H8Tla6F5iT80BZiwsZFj3OIb36GA7jlLWnN27EwO6xfDM/HXsr2uwHcenaKF7iX8uLmJndR13n5NhO4pSVokI95ybwfbdtbzy3UbbcXyKFroXKK3cz4tfF3FBvy70S4yxHUcp6walxjG6ZzzPLypkV3Wd7Tg+QwvdC/x9bj4GuPucnrajKOU17h3Xi7219UzTJQFaTQvdstXFVby/vIRJw1JIjNUbPyt1UM+TorhiYDdeX7KJ9eW6cFdraKFbZIzhL5/mERsRzK2jdQEupQ51h/Nq6f/32VrbUXyCFrpF8/K2s3TDTu4Y24N2oboAl1KHio8K4bbR3Zm/ZjvfFVbYjuP1tNAtOdDQyGOfryUtPoIrB3azHUcpr3X9sGS6xoTxyKdraGjUJQGORAvdkjeWbKKoopr7z+ull/grdQShjkDuHZfBmq27+a9ebHRE2iQW7Kqu45kFBQxNi2NMRkfbcZTyeuf37cyAbjH87Yt8qmvrbcfxWlroFjw5L589NfU8eEFvvcRfqVYQER44vzfle2p5ftF623G8lha6h+WUVPHm0s1cOziJjJPa2Y6jlM8Y0K09F53alZlfFbGxotp2HK+khe5Bxhgemp1L+/Bg7jgr3XYcpXzOfeMyCA4K4KGPc3XN9BZooXvQhytKyNq0i3vOzdD7hCp1HDq2C+X2sT1YlF/OvLzttuN4HS10D9lTc4BHP1tLv8QYLtU7ESl13CYOTSa9UyQPf5JHzQFdjbE5LXQPmb6ggIq9tTx84ckEBOgboUodL0dgAA+PP4XiXfv5h75B+j+00D2gsGwP//52I7/KTNTVFJVygcGpcYzv34UXFq9n0w59g/QgLXQ3a2w03P9BDuHBgdylqykq5TL3n9cLR4Dw54/zbEfxGlrobvZe9hZ+2LCTP/yiF3GRIbbjKOU3OrUL5fax6Xy5tow5OVttx/EKWuhuVL6nlkc/W8vAlFguz0y0HUcpv3P9sGR6d27Hgx/lUrX/gO041mmhu9Ejn+Sxv66BRy/qo1eEKuUGQYEBPH5JXyr21vL4HF1iVwvdTRbllzF7ZSm3jk6je8dI23GU8lt9EqK5YXgKby7dzNKiHbbjWKWF7gb76up54MMc0uIjuGWU3rhCKXe746x0EmPDuO+D1W363HQtdDd4Yk4+xbv28+hFfQgJCrQdRym/Fx4cxKMX9aGovJoZCwttx7FGC93FvltfwSvfbeS6ockMSo2zHUepNmNEj3guHtCV5xetJ6ekynYcK7TQXWhvbT13z1pFSocI7jk3w3YcpdqcB8/vTVxkMHe8s6JNTr1oobvQXz9dQ2nlfv5+WV/CgnWqRSlPiwkP5olL+1FQtpcnv8i3HcfjtNBdZPG6ct76YTNTRqRyWlKs7ThKtVkj0+O5ZnA3XvpmA0va2FkvrSp0ETlXRPJFpFBE7m3h9d+JSJ6IrBKRBSKS5Pqo3qtyXx33zFpFj46Rus65Ul7g/vN60S02nDvfW8memrZzwdFRC11EAoEZwDigN3CliPQ+ZLPlQKYxpi8wC3jC1UG9lTGGu2etYkd1LU9d3p9Qh061KGVbeHAQT13ej9LK/TzchtZ6ac0IfSBQaIwpMsbUAW8D45tvYIxZaIzZ53y4BGgzC36/tmQTX+Rt555zM+iTEG07jlLK6bSkWG4d1Z33sov5aEWJ7Tge0ZpC7wpsafa42Pnc4dwAfN7SCyJyo4hkiUhWeXl561N6qbzS3fzl0zWM7hnPpGEptuMopQ5x+9genJ7cnvvfX01R+V7bcdzOpW+Kisg1QCbwt5ZeN8bMNMZkGmMy4+PjXblrj9tXV8/Ut34kJszB3y/rpzetUMoLBQUGMP3KUwkOCuC2N5f7/amMrSn0EqD5UoEJzuf+h4iMBf4AXGiMqXVNPO9kjOHBj3LZUFHNM1f012VxlfJinaPDeOry/qzZuptHPvHv+fTWFPoyoIeIpIhIMHAFMLv5BiJyKvBPmsq8zPUxvcubP2xmVnYxvx7dnaFpHWzHUUodxeiMjtw0MpU3lm7m45WltuO4zVEL3RhTD0wF5gJrgHeNMbki8rCIXOjc7G9AJPCeiKwQkdmH+XQ+L2vjTh6ancuonvH8dqyeoqiUr7jz7J5kJrXn7lmryC31z6UBxBhjZceZmZkmKyvLyr6P1/bdNZz/7DeEBwcy+7bhRIc7bEdSSh2Dsj01XPjstwQGCLOnDvPJ6VIRyTbGZLb0ml4p2ko1Bxq4+fVsqmvrmXltppa5Uj6oY1QoMyecRuvNG+sAAAm8SURBVMXeWm5940cONDTajuRSWuit0Nho+P27K1mxpZInL+tHz5OibEdSSh2nvgkxPH5JX5Zu2MmDH+Vga5bCHYJsB/AFj81Zy6ert/KH83oxrk9n23GUUifol6d2Zd32Pfxj0Xq6RIfx6zN72I7kElroR/Ha9xuZ+VUR1w5OYvIIvXhIKX9x1zk92VZVw5Pz1tEpOtQvbuSuhX4EH60o4cHZuZyZ0ZE/XdBbb/SslB8RER67pC/le2u57/3VxEeGMDqjo+1YJ0Tn0A9jTs42fvfuSk5PjuW5qwYQFKiHSil/ExwUwPPXnEavzlHc/Ho23xRU2I50QrSlWrAov4xfv/UjfbpG86/rTtebVSjlxyJDgnh10iBSOkQw+dVlfFfou6WuhX6I+XnbufG1bNI7RfGfSQOJDNFZKaX8XWxEMG9MHkS32HAm/WcZ36/3zRtjaKE388HyYm56PZuMk6J47YZBRIfpueZKtRVxkSG8OWUwie3Due7fPzA3d5vtSMdMC93pP99t5I53VjIwOZY3pwwmNiLYdiSllId1iAzhnZuGkNG5Hbe8ns2bSzfbjnRM2nyhH2ho5KHZufxpdi5n9e7Ev68/XadZlGrDYiOCeWvKIEamx3P/B6t58ot8Ght94+KjNl3oO6vrmPDyD7zy3UYmD0/h+asH6C3klFKEBwcxc0Iml2cm8OyXhUx+NYuqfd5/b9I2W+g/bt7Fhc99Q/bmXTx5WT8eOL+3npqolPqJIzCAxy/pyyO/PIWvC8q54LlvvH6VxjbXYPUNjTwzfx2XvfA9xsC7Nw3hktPazC1QlVLHQES4dnAS79w0hLr6Ri6a8R0vLF5Pg5dOwbSpQl++eRcXPPctz8wv4MJ+Xfj89hH0T4yxHUsp5eUGdGvPp78ZzpiMjjz2+Voue+E78kp32471M21iPfTSyv1Mm1/Au9lb6BgVwkMXnKyLbCmljpkxhtkrS3lodi5V+w9w1aBu3D42nQ4eXFf9SOuh+/XpHFt27uNf327gjaWbwcCkYSnccVa6nsWilDouIsL4/l0Zld6Rp+ev47Ulm/hvdgnXDO7GlBGpdGwXajefv43Qaw40sCi/jPd/LGH+mu2ICBef2pXfju1BQvtwl+9PKdV2rS/fy4wvC/lwRQkBIozt1YlfnZ7I0O5xhAS554y5I43QfbrQjTFU7K1j445qVmyuZOmGnXy/voLqugbiIoK5YmAi1wxOonN0mItSK6XUz23aUc2bSzfzXnYxO6vriAgO5Iz0eE5Lak//xBjS4iOJCXe4ZMVWvyr0d5Zt5p+Li6iuq2dPTT376hp+ei05LpwhaR34RZ/ODE6N1dMQlVIeVVvfwLeFFcxfU8aitWWUVtX89FqoI4C4iBBCHAHcMTadC/p1Oa59+NUcemxECL27tCMiOIjI0CAS2oeRHBfByV3aWZ+/Ukq1bSFBgYzJ6MSYjE4AlO2uYVVxFZt27mNr5X52VtdR29BIjJvuSexzI3SllGrLjjRC1zkJpZTyE1roSinlJ7TQlVLKT2ihK6WUn9BCV0opP6GFrpRSfkILXSml/IQWulJK+QlrFxaJSDmw6Tj/egegwoVxXMVbc4H3ZtNcx0ZzHRt/zJVkjIlv6QVrhX4iRCTrcFdK2eStucB7s2muY6O5jk1by6VTLkop5Se00JVSyk/4aqHPtB3gMLw1F3hvNs11bDTXsWlTuXxyDl0ppdTP+eoIXSml1CG00JVSyk/4XKGLyLkiki8ihSJyr+UsG0VktYisEJEs53OxIjJPRAqc/23vgRz/EpEyEclp9lyLOaTJdOfxWyUiAzyc6yERKXEesxUicl6z1+5z5soXkXPcmCtRRBaKSJ6I5IrIb53PWz1mR8hl9ZiJSKiI/CAiK525/ux8PkVEljr3/46IBDufD3E+LnS+nuyOXEfJ9oqIbGh2zPo7n/fk93+giCwXkU+cj91/vIwxPvMHCATWA6lAMLAS6G0xz0agwyHPPQHc6/z4XuBxD+Q4AxgA5BwtB3Ae8DkgwGBgqYdzPQTc2cK2vZ1fzxAgxfl1DnRTrs7AAOfHUcA65/6tHrMj5LJ6zJz/7kjnxw5gqfM4vAtc4Xz+BeAW58e3Ai84P74CeMeN32OHy/YKcGkL23vy+/93wJvAJ87Hbj9evjZCHwgUGmOKjDF1wNvAeMuZDjUe+I/z4/8Av3T3Do0xXwE7W5ljPPCqabIEiBGRzh7MdTjjgbeNMbXGmA1AIU1fb3fk2mqM+dH58R5gDdAVy8fsCLkOxyPHzPnv3ut86HD+McAYYJbz+UOP18HjOAs4U8QFt7s/tmyH45GvpYgkAL8AXnI+FjxwvHyt0LsCW5o9LubI3/DuZoAvRCRbRG50PtfJGLPV+fE2oJOdaIfN4Q3HcKrz191/NZuSspLL+evtqTSN7LzmmB2SCywfM+f0wQqgDJhH028DlcaY+hb2/VMu5+tVQJw7crWUzRhz8Jj91XnMnhaRkEOztZDblZ4B7gYanY/j8MDx8rVC9zbDjTEDgHHAbSJyRvMXTdPvUNbPC/WWHE7PA2lAf2Ar8KStICISCfwXuN0Ys7v5azaPWQu5rB8zY0yDMaY/kEDTbwEZns5wOIdmE5FTgPtoyng6EAvc46k8InI+UGaMyfbUPg/ytUIvARKbPU5wPmeFMabE+d8y4AOavtG3H/wVzvnfMkvxDpfD6jE0xmx3/gA2Ai/yf1MEHs0lIg6aSvMNY8z7zqetH7OWcnnLMXNmqQQWAkNomq4IamHfP+Vyvh4N7HBnrkOyneucvjLGmFrg33j2mA0DLhSRjTRNC48BpuGB4+Vrhb4M6OF8tziYpjcQZtsIIiIRIhJ18GPgbCDHmWeic7OJwEc28h0hx2xggvPd/sFAVbNpBrc7ZL7yIpqO2cFcVzjf8U8BegA/uCmDAC8Da4wxTzV7yeoxO1wu28dMROJFJMb5cRhwFk3z+wuBS52bHXq8Dh7HS4Evnb/xuNxhsq1t9j9moWmuuvkxc+vX0hhznzEmwRiTTFNHfWmMuRpPHC9XvaPrqT80vUu9jqY5vD9YzJFK0xkGK4Hcg1lomvtaABQA84FYD2R5i6ZfxQ/QNDd3w+Fy0PTu/gzn8VsNZHo412vO/a5yfiN3brb9H5y58oFxbsw1nKbplFXACuef82wfsyPksnrMgL7Acuf+c4AHm/0M/EDTm7HvASHO50Odjwudr6e68Wt5uGxfOo9ZDvA6/3cmjMe+/537G8X/neXi9uOll/4rpZSf8LUpF6WUUoehha6UUn5CC10ppfyEFrpSSvkJLXSllPITWuhKKeUntNCVUspP/H961InuZlWRnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x=np.linspace(0, 400 - 1, 400, dtype = np.int64)#返回区间内的均匀数字\n",
    "# print(x)\n",
    "w = 0.54 - 0.46 * np.cos(2 * np.pi * (x) / (400 - 1))\n",
    "plt.plot(w)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9DryS5jYTFN7"
   },
   "source": [
    "**3. 对数据分帧**\n",
    "\n",
    "- 帧长： 25ms\n",
    "- 帧移： 10ms\n",
    "\n",
    "\n",
    "```\n",
    "采样点（s） = fs\n",
    "采样点（ms）= fs / 1000\n",
    "采样点（帧）= fs / 1000 * 帧长\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f3d59-6jTFN7"
   },
   "outputs": [],
   "source": [
    "time_window = 25\n",
    "window_length = fs // 1000 * time_window\n",
    "#保持window"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kx8VYd5BTFN-"
   },
   "source": [
    "**4. 分帧加窗**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "colab_type": "code",
    "id": "6o3Yn8qmTFN-",
    "outputId": "d1a41ffb-d7e6-450e-db91-997ff527d346"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分帧\n",
    "p_begin = 0\n",
    "p_end = p_begin + window_length\n",
    "frame = wavsignal[p_begin:p_end]\n",
    "\n",
    "plt.figure(figsize=(15, 5))\n",
    "ax4 = plt.subplot(121)\n",
    "ax4.set_title('the original picture of one frame')\n",
    "ax4.plot(frame)\n",
    "\n",
    "# plt.show()\n",
    "# 加窗\n",
    "\n",
    "frame = frame * w\n",
    "ax5 = plt.subplot(122)\n",
    "ax5.set_title('after hanmming')\n",
    "ax5.plot(frame)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VC_PGtWoTFOB"
   },
   "source": [
    "**5. 傅里叶变换**\n",
    "\n",
    "所谓时频图就是将时域信息转换到频域上去，具体原理可百度。人耳感知声音是通过"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "colab_type": "code",
    "id": "5CnLQZnHTFOC",
    "outputId": "3d47a819-10f7-4221-efe4-5d9fd950069c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.fftpack import fft\n",
    "\n",
    "# 进行快速傅里叶变换\n",
    "frame_fft = np.abs(fft(frame))[:200]\n",
    "plt.plot(frame_fft)\n",
    "plt.show()\n",
    "\n",
    "# 取对数，求db\n",
    "frame_log = np.log(frame_fft)\n",
    "plt.plot(frame_log)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bxLC8NKeTFOF"
   },
   "source": [
    "- 分帧\n",
    "- 加窗\n",
    "- 傅里叶变换"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9zrhkEr_TFOG"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.io.wavfile as wav\n",
    "from scipy.fftpack import fft\n",
    "\n",
    "\n",
    "# 获取信号的时频图\n",
    "def compute_fbank(file):\n",
    "\tx=np.linspace(0, 400 - 1, 400, dtype = np.int64)\n",
    "\tw = 0.54 - 0.46 * np.cos(2 * np.pi * (x) / (400 - 1) ) # 汉明窗\n",
    "\tfs, wavsignal = wav.read(file)\n",
    "\t# wav波形 加时间窗以及时移10ms\n",
    "\ttime_window = 25 # 单位ms\n",
    "\twindow_length = fs / 1000 * time_window # 计算窗长度的公式，目前全部为400固定值\n",
    "\twav_arr = np.array(wavsignal)\n",
    "\twav_length = len(wavsignal)\n",
    "\trange0_end = int(len(wavsignal)/fs*1000 - time_window) // 10 # 计算循环终止的位置，也就是最终生成的窗数\n",
    "# \tprint(range0_end)\n",
    "\tdata_input = np.zeros((range0_end, 200), dtype = np.float) # 用于存放最终的频率特征数据\n",
    "\tdata_line = np.zeros((1, 400), dtype = np.float)#窗口内的数据\n",
    "\tfor i in range(0, range0_end):\n",
    "\t\tp_start = i * 160#步长10ms所以\n",
    "\t\tp_end = p_start + 400#窗口长25ms\n",
    "\t\tdata_line = wav_arr[p_start:p_end]\t\n",
    "\t\tdata_line = data_line * w # 加窗\n",
    "\t\tdata_line = np.abs(fft(data_line))\n",
    "\t\tdata_input[i]=data_line[0:200] # 设置为400除以2的值（即200）是取一半数据，因为是对称的\n",
    "\tdata_input = np.log(data_input + 1)\n",
    "\t#data_input = data_input[::]\n",
    "\treturn data_input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JDPZHdHiTFOI"
   },
   "source": [
    "\n",
    "- 该函数提取音频文件的时频图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "colab_type": "code",
    "id": "_YKOAoZhTFOJ",
    "outputId": "6b2ef385-a873-4daa-837b-12faf2ac3dc6"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "filepath = 'test.wav'\n",
    "\n",
    "a = compute_fbank(filepath)\n",
    "# print(a.shape)\n",
    "plt.imshow(a.T, origin = 'lower')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(978, 200)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Gxj0Uv_0TFON"
   },
   "source": [
    "## 2. 数据处理\n",
    "\n",
    "#### 下载数据\n",
    "thchs30: http://www.openslr.org/18/\n",
    "\n",
    "### 2.1 生成音频文件和标签文件列表\n",
    "考虑神经网络训练过程中接收的输入输出。首先需要batch_size内数据需要统一数据的shape。\n",
    "\n",
    "**格式为**：[batch_size, time_step, feature_dim]\n",
    "\n",
    "然而读取的每一个sample的时间轴长都不一样，所以需要对时间轴进行处理，选择batch内最长的那个时间为基准，进行padding。这样一个batch内的数据都相同，就能进行并行训练啦。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bTMcXzMgTFOO"
   },
   "outputs": [],
   "source": [
    "source_file = 'data_thchs30'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ECHpbyuMTFOQ"
   },
   "source": [
    "#### 定义函数`source_get`，获取音频文件及标注文件列表\n",
    "\n",
    "形如：\n",
    "```\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_0.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_1.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_10.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_100.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_102.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_103.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_104.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_105.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_106.wav.trn\n",
    "E:\\Data\\thchs30\\data_thchs30\\data\\A11_107.wav.trn\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 73
    },
    "colab_type": "code",
    "id": "IC8m_vKJTFOR",
    "outputId": "48bdcbca-3a76-49ea-ed0c-676dec6c486c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['data_thchs30/data/D11_982.wav.trn', 'data_thchs30/data/D12_841.wav.trn', 'data_thchs30/data/B8_298.wav.trn', 'data_thchs30/data/D6_889.wav.trn', 'data_thchs30/data/C32_692.wav.trn', 'data_thchs30/data/A14_44.wav.trn', 'data_thchs30/data/B31_478.wav.trn', 'data_thchs30/data/A11_184.wav.trn', 'data_thchs30/data/A8_220.wav.trn', 'data_thchs30/data/B12_271.wav.trn']\n",
      "['data_thchs30/data/D11_982.wav', 'data_thchs30/data/D12_841.wav', 'data_thchs30/data/B8_298.wav', 'data_thchs30/data/D6_889.wav', 'data_thchs30/data/C32_692.wav', 'data_thchs30/data/A14_44.wav', 'data_thchs30/data/B31_478.wav', 'data_thchs30/data/A11_184.wav', 'data_thchs30/data/A8_220.wav', 'data_thchs30/data/B12_271.wav']\n"
     ]
    }
   ],
   "source": [
    "def source_get(source_file):\n",
    "    train_file = source_file + '/data'\n",
    "    label_lst = []\n",
    "    wav_lst = []\n",
    "    for root, dirs, files in os.walk(train_file):\n",
    "        for file in files:\n",
    "            if file.endswith('.wav') or file.endswith('.WAV'):\n",
    "                wav_file = os.sep.join([root, file])\n",
    "                label_file = wav_file + '.trn'\n",
    "                wav_lst.append(wav_file)\n",
    "                label_lst.append(label_file)\n",
    "            \n",
    "    return label_lst, wav_lst\n",
    "\n",
    "label_lst, wav_lst = source_get(source_file)\n",
    "\n",
    "print(label_lst[:10])\n",
    "print(wav_lst[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bMZGx3oaTFOT"
   },
   "source": [
    "#### 确认相同id对应的音频文件和标签文件相同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rx1aplrVTFOT"
   },
   "outputs": [],
   "source": [
    "for i in range(10000):\n",
    "    wavname = (wav_lst[i].split('/')[-1]).split('.')[0]\n",
    "    labelname = (label_lst[i].split('/')[-1]).split('.')[0]\n",
    "    if wavname != labelname:\n",
    "        print('error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FRhD_BV8TFOV"
   },
   "source": [
    "### 2.2 label数据处理\n",
    "#### 定义函数`read_label`读取音频文件对应的拼音label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "Paw8s3A8TFOW",
    "outputId": "66099c8d-c4a7-4cb3-ce73-dd3ec48e1896"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ru2 guo3 xia4 dao4 gu2 di3 dun4 jue2 han2 qi4 xi2 ren2 tai2 tou2 si4 gu4 yin1 sen1 sen1 de5 tu3 bi4 que4 you3 qun2 mo2 ya1 ding3 de5 jing1 xin1 dong4 po4 zhi1 gan3\n",
      "\n",
      "13388\n"
     ]
    }
   ],
   "source": [
    "def read_label(label_file):\n",
    "    with open(label_file, 'r', encoding='utf8') as f:\n",
    "        data = f.readlines()\n",
    "        return data[1]\n",
    "\n",
    "print(read_label(label_lst[0]))\n",
    "\n",
    "def gen_label_data(label_lst):\n",
    "    label_data = []\n",
    "    for label_file in label_lst:\n",
    "        pny = read_label(label_file)\n",
    "        label_data.append(pny.strip('\\n'))\n",
    "    return label_data\n",
    "\n",
    "label_data = gen_label_data(label_lst)\n",
    "print(len(label_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EAC0XpSeTFOZ"
   },
   "source": [
    "#### 为label建立拼音到id的映射，即词典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "b_tPrz9sTFOa",
    "outputId": "67c85692-c6e3-42d5-d51d-7f58e1643b85"
   },
   "outputs": [],
   "source": [
    "def mk_vocab(label_data):\n",
    "    vocab = []\n",
    "    for line in label_data:\n",
    "        line = line.split(' ')\n",
    "        for pny in line:\n",
    "            if pny not in vocab:\n",
    "                vocab.append(pny)\n",
    "    vocab.append('_')\n",
    "    return vocab\n",
    "\n",
    "vocab = mk_vocab(label_data)\n",
    "# print(len(vocab))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1Rr9kgrpTFOg"
   },
   "source": [
    "#### 有了词典就能将读取到的label映射到对应的id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "id": "LfgQDpjbTFOg",
    "outputId": "6a561916-7960-4c2e-ef46-241a8aa9a872"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ru2 guo3 xia4 dao4 gu2 di3 dun4 jue2 han2 qi4 xi2 ren2 tai2 tou2 si4 gu4 yin1 sen1 sen1 de5 tu3 bi4 que4 you3 qun2 mo2 ya1 ding3 de5 jing1 xin1 dong4 po4 zhi1 gan3\n",
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 18, 27, 28, 29, 30, 31, 32]\n"
     ]
    }
   ],
   "source": [
    "def word2id(line, vocab):\n",
    "    return [vocab.index(pny) for pny in line.split(' ')]\n",
    "\n",
    "label_id = word2id(label_data[0], vocab)\n",
    "print(label_data[0])\n",
    "print(label_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5jLX4T9hTFOo"
   },
   "source": [
    "#### 总结:\n",
    "我们提取出了每个音频文件对应的拼音标签`label_data`，通过索引就可以获得该索引的标签。\n",
    "\n",
    "也生成了对应的拼音词典.由此词典，我们可以映射拼音标签为id序列。\n",
    "\n",
    "输出：\n",
    "- vocab\n",
    "- label_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "uH4Wq9BdTFOo",
    "outputId": "d400a47b-a2ba-4977-edb8-c9599854273b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ru2', 'guo3', 'xia4', 'dao4', 'gu2', 'di3', 'dun4', 'jue2', 'han2', 'qi4', 'xi2', 'ren2', 'tai2', 'tou2', 'si4']\n",
      "lao2 qu3 tou2 hua4 yu3 hen3 shi2 cheng5 you4 si4 hu1 yan2 you2 mo4 jin4 ta1 yi4 ku3 shi2 tan2 de5 chang4 kuai4 si1 tian2 shi2 que4 tun1 tun1 tu2 tu3 xian3 ran2 you3 nan2 yan2 zhi1 yin3\n",
      "[211, 212, 13, 213, 214, 189, 75, 215, 118, 14, 216, 217, 186, 218, 219, 137, 53, 220, 75, 221, 18, 222, 223, 87, 224, 75, 21, 225, 225, 226, 19, 227, 228, 22, 159, 217, 31, 229]\n"
     ]
    }
   ],
   "source": [
    "print(vocab[:15])\n",
    "print(label_data[10])\n",
    "print(word2id(label_data[10], vocab))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7nkZQNioTFOr"
   },
   "source": [
    "### 2.3 音频数据处理\n",
    "\n",
    "音频数据处理，只需要获得对应的音频文件名，然后提取所需时频图即可。\n",
    "\n",
    "其中`compute_fbank`时频转化的函数在前面已经定义好了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "g5s_Skl3TFOr",
    "outputId": "786bd9da-e690-494e-cc12-33b8813c9c2d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_thchs30/data/D11_982.wav\n",
      "(1041, 200)\n"
     ]
    }
   ],
   "source": [
    "fbank = compute_fbank(wav_lst[0])\n",
    "print(wav_lst[0])\n",
    "print(fbank.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 139
    },
    "colab_type": "code",
    "id": "tymo7MeETFOu",
    "outputId": "3ad3cbe9-f721-4e54-faef-1475c532e7f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAABfCAYAAAATSIcRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOy9ya9lV3bm99vNaW/7+hc9g00ymUplY6mEkgSoyiVNPLAn/g889Nx/kweeGvDAZcBdQZZUciozSWaKSUYw+te/2552Nx6sEzdSsFMFGAUUJcQGCAb5Xtx77jlrr/2t7/vWuirGyPv1fr1f79f79c9r6f/UF/B+vV/v1/v1fv3HX++T+/v1fr1f79c/w/U+ub9f79f79X79M1zvk/v79X69X+/XP8P1Prm/X+/X+/V+/TNc9j/1BQCk6Sjm6ZyQarSLRA26C/hMoz0EAyqA7gMhkfNIuYAbGVQA0waiUSgfCYlGuQgKfKqwbUTVHVhDTAw+UZjGg1KEVKN8RPcBnxu0i/SlwtaRYBXayb+jBd2Bz+R6VQCfy/9LtgFXaEIKUYHpIFiISUTbQIwKGi2fycnfDQaUlz+bLqJCxJUa5SEaIMprJNtIP1FEBbqX348GogbTDNcCKBcJqUJ3kWgUxIiKoIbPpULE5wrlICTyOspDyCCaiO4UIQHdQkjl50R58aggqYZrjqD2e3zQhKAwG40vIjb1ODf8QlToWr39IyrKa8XhGQYr1x6St58pyvsBuu6IeUrU795f+UhUCjREo9BdIFh5fZfLdb99JtFGMBHVSoxoJz/zowBOgw0YE4lrQ7L2uNLsfidquaakirhseD8l16E9uGJ4vcFcFopIkjj6zqI6hfKQ7bU0fUJ0Gt2++/1g5TW0iygX6eYa5eTzRy33SHcSs1GDzxVR8w9iRjn5+zDcE63k/gT5c9TyHvhANBoVIiHV9CN2gaI7ee7xt3a9cvL+IYFYBqJXmFpiLup3saDgH/y/qH/rNeNw/1JgeJbRgK2GfeKGmG3lNXwGaDA1+HmAWqMd6D6CUrhCnqWykdhr0qXEdVRDwPNbsTX8GUAH6Ivf2mepXIdpwG487ZEGr9AOkk2gm2nZW+7d3o2G3X4zTQAt99tnBu2C3PffMhiqINe2y01DzIZU8tA/uIcxyl6wavfM3t5LW3nZu/xWnCfvsLduHShFN7XYRh6IcpH19vVVjPHo/yuvfieSe57O+eOP/xu2j6eUL7d0ezm+MOTnNWhJ2nrbsv1oTnbT4UaWYBXVkSVbBWwlG9Vn8rvlWUd9nGLaSHbdEnJDSDTBKPqJbGw0JCuHLwy6C7R7CenKcfmTDFtFRmeBbNFz8dMc7WD82rO+Z0jXEZ/B4geByRPD6b9bc/4vJ/hMNjFI0G0fOvbuL7m9mpA/S/FZJKSRybea5gCKcwmK6TMHCtqZJl0H1vct3VQ26uRZYPmhpptHRi8V1V1JxEc/92yPNekmYptIdutY3085+Nktq+/PcUNyIML+lxvWH5RsTw3JNrL6CNKlYvrUc/bHipAHZr+2uFI2Y30c6fY96bUhXSnSpURytooEAx/8t1/x+fkdtpcl468Tth941Kzjg9NrzlcTtjcF2auU7AZQUF4EglEsP4HRS1h9DMlaMXoViQr2vm5QfcCVFu0jpna0exkqQv5mg5vn+ERjK0c3S0FDdWiZfVPz8i9KXB5RQL/vICjQkdE3CfVpILvWlG8ii+9DPG042N9w+WIPlXvKX2ckG7DbuEui7b4iv4q0c7XbdP1Y7sv6ex6z1rijHnNr8RPP6cMbzi9nZN/kJBso//yCuktYXY8onqYUFxHl5bXXjxWnf9mhXeTiDzKStRxqyUbiYP7VlupuweSLa178V8c0BxFfRNRBS/p1QbqEdBkpLzym8WQXW9afzEi2gZAoko2jOUiYfLWkOxqhXWD1MOfyzzuyUUf/fESylOSje+imkdFrRbaQz3/zw8jkkwXLZUn6bUZxrobfFYCjfCTZQjdRbB9GIpBsFQSYPQksPtWYRu5ZcxSINvLxf9/w5L8u2PtcUR8rJs8Dton4THHzA8X4OVz/SY+5TkBFZr9RpGsBWKu/2OKaBH2dkF1rDn/p6Eca3UdMG1l8YtEtjM88LlfUB5psEbn+EeTXctiiwOUweRYZnTue/Zcaxj2jL3KKi8jiU8ivFM1BpDgXUBfNkJNuIraV2LV1QHdBnte6o5ulhFQTjSK/bFg/KrFNILvpafcTyudbYqJZfTwiW3jyNxVmsaH58BC77enmGcoFurnFZYri2lM8uSHMSrq9DFdoivMG1Xua05L0tiMmGrtouPiXc8qLwPjJivUnU/7yf/jvnv2uvPqdoGWiUYQyxdaBmBjQCtMG2qMcvenQ2xZ1uyK/bIhW4QpN+XRBUke0k3/ym46+VJgu0hylaB/Jrxq6vRTdeJJVh208ydqj+wABmqOU9LbFFYZ06TC1IyoYnQX6UpFebCVIACLkt5H6SOFKhW4V2U0kZJbyPNBNI91eJFtExi8Dauwo0h5lA/0k4EaR/EqQlCA2ScDJ1uFyRTvXdBNNOwcU1MeBaKD6qMMd9aw+9RChuICbTw0oGL/s6AvN+oF83tsfzQUF1IHZ0wYV4fazMVEr3EhQSXoryKOdakElTtBCN5NN40YR0kBIBGE1hwqfK3Qf2d7V/NWvP2S7zMFEQVuN4vRwyYvLPbwXZNQdevqJ/P1urGj2FbpXbB4AAXwa2TyU9232U1xpQSvSszXNYY4Z0Pnmwyk+0fjCEDJDflmTLnpsE3HjhHT5W2jaS2I3S0t9JxBGnubY0xwpzMMtMSquricksxa1SJg8C4LQPIzP3A65Bwv9BNo5uFKqG9vEHXp9u1QWyIxHn2f040h9LIdg3SSoxtBPIslWEllzoBi9iPRjQzu32Ar6ibya6eU9u3lGunAsf3yIreVZ6R7stzmjVxGXy+fMrluiUVQPp0x/eYXuA8nGoYK8vx9lKB+xi4Z2rkjLDvtXE4jgyogrJIEpYHsv7kBAstEsLscS51eK7b1IOxcUmy0D2TLSHCj6KditIl0q+pEcUPWhJr+UxO7KiK0U2aXh+oclqldsHsghMn7R0Mw1Vz8WFN4cKspZTbpQTL9RmAaqY83mgcKfleTjFpDDNVk7fKoorjq6icanUF4FqkNNP1Kkq0hSS9mge4m9fiwx2s0UdtPvqkGfQLoJuJHEebZQRAPzbzrGrz0+k4PN1AHTRkZfnhOtxrSe9iDHVg6facqnK9wowbYBu/W0+wnJ2qNCYP3hiHQVSBcdfpxSfe9oeAYJppXD2VZDJQCsfnSIL61UZUYRjaY5LsmuW9w4IRjN9oMJ2VLyner9rpL7Xes7kdxVhGA1yaojJIZm35Je1+g2EMqEmFr86QHRaKKWUzlmCfN/f0Z20+FTjeoDR391Q3HekN305JcdbpSQbB1oRbuX4UpD8WqNLwzpsiO/7mgPctDQTwz9JCFdR7qJ0EHd0Yikitgq0s403UTtNn/II91Msb2bsfhYS2ndQjdVuEyhVCTE4d95JGZB0FguG2b8xhMNtHsJUSvCQMfMvwnYCnSnKM96oRM6eUxRQzeF8jySriPNYYJtI9NnLcpDceWlyvGwepQL4nCRZBvQPaw/kNIYBdk6Ek0kThz9kPjlTUBtrSRNJaWtaSJ9qZl+63n04Ips1EGnqY8joxeaN2d7PD6+xj0Zo0dul0iSrWy08ZtANw/YShFtJGRSzpsO8qtOqqc+0NybMvrmluo4Idk4tIt0s6FKO0nZPBrhMyOo/vUal8v19acdauTAS9KxW4WqDcWZoZtHYlDEjUWpyHxaYSpFdaxJtpF0HaiOLPWJHNblpR8OSEi2kF9CPxaE6iaB5DIhWogRGmfR9ytGLxXpQmF1YH9aEU3EbpUk9kOhJppDRX7TM3pRoVwkXUYmzx19IYgxWXWkV1vSlaCJZK3wZaC/38nPt2D6SHOcEQckvf30AN16kuuKdp5QvmlJnl/iRgZfpkxfONplzvZHDcop8hvF9KlQMUQYP1OkmyBxNZPEGBtDfRKZfQ3zr6UqyJbyb6FLod0PNEeByTOheKKWg6I5DEQLplE0jzqyVcR0EgPNvmL5cfEP9nx2EzmdrWnuelyhsE1k+lw+f5z3tM/HhDSSriLtXoKtA81Bukvm1bFm8sKR3wZ8JjQdOu4oofL1QAd66GcJsfREr7ANNDNNLALtXCrIaKAfG5aPLe0erO/r4SD2dPf3CVZhv36N7gK684yfrGhPR9h1R9QKnxuym57s1ZL67pjs1u32ky8MzYElva4IqTAI9YMJ7dxQ3DjauWHymxX9WGLdVoFuJnyjLyzKRUzrhZp2kXTRU30wEyrvH1nfjeReywkdMkM0itGbFjfNSFadlOyzjO6ooJsmuNKgYsSNU9Y/OmZ7LydaxerDgvX35mzvFSQ3Fe1eSnWckD6/oR9b3EjTTQzt6Zjx5+e0BxnVcSo3zUVGT1fYymNaKK4c859f4wqDyyXo3vLhphcOr3xpyBaRbOnRDk7/LyeoF+FttYmcPzlEv87RtYY0CGc9VALdWG69aQJJFUjXkZAouonCZ1CeKbqZRdcauzLoWg4QlCBJl6kdf79+kGHbSDcVDSJdOHz6ju9ePbT4DLIbRX0q9Eo3UqheoUwkJJBfKjb3QXkF5q1mEfFZpDqVQ83lmhAVRdaTHjQoD+uPPON5Re0S/CgQOoPZag5/MSDJTDZ/equJFrJrTXqr0D2UFz31cUqycTsEUz2eky29lKy5IiQKFSOjV+2OKnG5oj0eCaeqI/Y8JTYGM+uo7zm6Pb/TKEyjCC9LVFDE25Rtk9Lve/JbQdYqRMpzR7qMtPuK5WOLaQS1N/tyeGS3kezGYNaafiYVVJI7bpYjYpSqKNlAYjzbNgXAjSLZSiq2MCRAVxi6vQxfiP6xuSv8abbwmG1Hdziim2iqOxGfR9Jbw/G/TfDJO5pNd6JB6F6Scj9JWPzenJAoXGHoHx2h20C3n2KaiMo8sdOkS+Gyu4kS/r9TtPvymqaX5z093AISCz6FdiYx1o31jit3JYyfa8YvNNu7UnUIvy2UYT/3dPOAqgzdRNEdBNESLOQLCf7RS0VxpghWnrmuNLaOtDPF+r5h/1eBw6MV5eMV428N9ZHEga0jyUbuPxEIsLknCXHyypEt5YDyiTyz6i6EJNJPhNNWlYHW0O5F0m1EdVoOqOEwr45lT6dLocu0E+68m6dk1y3dD+7TTw3RCorWLhKNFgQ+ULLtvdnu8E22jn6W4kpNed6zfTyheHqL6QOjLy8gQrNnBITdrFEu0k8MpvFSGfz1N3QzS3qxJaQaojwLX8g+X9//x1n170RyJ0no9lI29zL6saUvLf3Y0u3n6LbHVI7821u0j2SXDQSIiSZZe6a/XqCCcM+6j9gm0B2NUDEy+/Wa/nSOGxlmf/Oa4rIne71h+4Nj0lUvQd56olZsP5xCFBQcEgVKYWs5LatjTXHjMW3ED3RFO49s7kmARgOv/8yy/7kkwc09jU089qAmpJGjn0Vo5OHYSgI93QRm3zps5UlX/U4U6seykfuRCMKhCJhmQHJZZPwisnoklQURspWnPO9pp5pk62n2NN3MSuLy0I8U+W0gv464AtKFElRdRcLIE3tNsoHmKJKuFH7iiVaSuq1EXAOhL8qLntdXc0JUuM6IMBYUfW+5Wo/46LPXKCtVwuJjLeJpK/ejftTLc3srvAHtniW/6WkOkh3CMW1A+cj4qwXai/DVF5rN/YzRy4p00WKbSLSaYCOmUfhRYPqrhNBrVOFRThHSSLsXcXnEH3Yw7dl7fEu9zkFHmn1FO1PUB4b60OKKQeDy8ozy6wgDVeNyhc8HPlaB3Sj6RcbdgyW8KKgeOtaPA4XtqbYZutG4445gFNNvwDbymbupaD/NQaQ+ViR1pD7SrB9Y+r2C7HzD7MsF2bXCtArdKc7/lVQS0YBtIyFTjH5zQ0g0uouMfnW+Ax/ZxZbmKEP7SHrTsXpoiZ1GJWFnAAiJ0BXZDRx84WlnGp8q8nON0YF03tLNYe83ndAZlSR+7SL5tdAw7V4cBGZFdgO+AFOLUGlXhvRWo5wiW0bwUN/16A6amZSHwcL2gSTPp7++swM80UhS3dzVXF1N0CqyeSSAq50o2pmg6XQr92/63KG8mBr6UrM9MbInFrLHTK2wlSJZDTRo7jGTHt0rXC7Cf32iCKmApOI60BwoXAm+UDs9w2eK5jijH1nKZ1u6WSpUcqrZPCxIFy3ZdY/PNCHVFGc1KiLV1K/OyC/aAfFHYpHiE031vSPGrwXUjr+4pPrBKdEoisuO9NWC0cuK+g8/JL/s2H40JWrRVcpzR7BiCnl7OP6u9d1I7lG4v3QtN1P3Ad0LqqofzmgPcrp7MxE1IoRU1OZ+QOKu0DRz+SjBKlxpSFaO7qigOcmwW8/qD+4SFdQPJpg6iJKt5KGbNlC+qlk9Llg9ynC5pn4wBSC/CSTbSHVo0L1wvL6ImFaRX0eKN1txzawUm4eKZAPHP2tptylaRzhsOfvPPabSuMOe6l7AF5F+pLj+zOIzjU80KiDCymWguBQxzfSR5MYQLBSXcSgdobgSNGg6uU/NviDAzR1LeeVZPzC0c3nw6SbunCXRSBI3jSQyu7To1JOuIulC0BpA8UIQQdTQT8OOp+ymhrzodmYBM3zuSdnQbDLWbSY0VALpUhJaUkfK84C9sRz9wtFPIrYCNwIi9CPL6Hkl17rqSZYtykeauxOhmJwc3PmtZ/1BibleyyFeO6F3eoWpNc1hJG4tsTGUr8zgAIp88D81KPP/Ll+DZcdtz57U2DoOz3eoZG4j/ViQYLYUlBwK0Wq6Y0eyNDx7ekR8UAPCQ9/UJWnmCGkgf5pRHWu6ucLUkli1i7hCknayEcS79+uW/S9rdOup70+o708AQf4+i5iVoTpVlOeiQ5SvalQvJX9+1dDf2ydYSJcOP8lJNl6oAiOOomTcEWvL5FncOXMOfhnEpRXl3iaDqFw1Kd1tjqnh/A8zTA2uUMx+s6UfKWwtFMuj/3FDupDP1M0gWUdxlDmFH8CI7hTTr1boVqP6oVK7dNgmiiPLie7wyWevJC4PBCiFRKoDgLpO0Z3EbbYKaA/5tQChbAHdVJNuI81cKv7xG7c7gNs9cbl184jpIt3U7mw16XJw5kSheNMVTF4JpakcO2F4c8fiCk1+02PrQPlyQ/VoRHZRU76s8JmWyn3TCd2SapQL1KfFQCdC+/gIe7WhOUrJrhrqeyN8bjCdCN4Amx8eUbxcY9qAXTSsf3iEvt3gcw0xYreefiyMxfaOJbtqRGvZ/hOgZaLVpCsnlkILtnKki1ZOq1VHsuxI/vJL7LbHXC5Jl452zzL+4or6wFK+api86PH5O2W7myX0I0N+3pKse4qzhnTZgRLFu91PBzFWVOto1LD5IF153MjQHCS4Qvi9t1xsdhtJVoIMq1PF5vGY/V874ZBryJaRqx9l2LxH60hcpKjKYBqFvUqkvM2FVrGNlIum8Zg+Uh8YqhPN9q4i2YiIGS24cWD9CGytqE4V7VzQRNSK1YOBmxvK1nZq2P+yFWqhUCSbwOhNS0iGqkFHjn7hBstdIGwSmkNFupBnoTrFwa886VLjS9kA6YKdo+dkumaUdfKemQiwy02BsoHj0YZQ24GXBbsFlylcoSjPFGd/ZNBv7Z4B0o1ANuWD2MQCbB6N6GZWqo+1p9k39KWi2TNkS8/6xycEC8FI6E6finbQ32/JrgzFS0t1z+PHgcO/U3z7X+QkmYN1wu2zPdKywyytWGytJJlunlBcy8ZOKkl2y8d6V7XYVhK/qTTKK1RjcGUgmXb4TcLkNxY0XN1OcN+MSRaG5kGPaSPpItLuCX8fjCJdB2wjNtdgoDpNuP5hgf3i6c4coDyc/LWAgNELTbqCZl/TF4puL6P++BAVItXdgpBqXKHRnaebJnLv5hkhFUrRvy4hDeIYslC+iVz+VKyH1ZGU9z6VBNp3FuUUZrDEVncixXVg81CybUgkDi7/YEw3CP8iXg7WTQUxFf4bBZf/YgZa9AcVoJsYOVQ8aC8CqtGBkAeyG0mw3VQqy9gZYnx7EIrmo52I0iEZtK+RvK9P37l6oo1s78q9zW7e0aDjJyuxIUbRzKIRpxhRPsP21LD80GJrqdjeWkjTlafdS8hfrtg+GmPqQMgtZlmL5TpGqsdT0sstrtCktw3JxtGXimzphYG4OyNdOtw4pXi5ISSK9OUCV8D0aSMHTS8ibb9XUL6pCXtjskUvOqMVlqKdJ5QXHhUi27sZ3eyfAnIHXGkYvRAEFRNNSAzZeYUvrPhLf/8T2v2M+mOxdBaXHevfPyJbeXTn8LnGtOJZR4naXZw3wq3fyWmOMnxm8Llm87CkOKtxucaNEnQfqU9z9FCSV8eWbqwp3zT0Y0U3VYx/+Yb6WJKrzySxpSsJ6O0dI77o3/JGAzTnI0b318Sxozv0uOOOdKXJLmVzBcvgjVUDMpIkYisJynYm3neiIluIUGga+bmtIt1YM3va0Y/FJeQzRV9Cc5AQjWza6thQHaf00zggPM3qkVhIdadQTmG3UJ9IRRJt5OyPtBxW1eBguDt8IAVaRc4vZ4RtQsgj2Y3mk5NLPr5zyZcv75DNG9woMvlWklo/VozeeKqTiHaK8pVwv+kSihdrQqKoHowGm6pl/Gwr1EcdSNbigJq8bDF9pDqydKOhQksUdqO4+FNHyCM2c3SPWpoTD1OHyj23n8lB0l3nMO5RnaLdpvipo5ux89ebJrA90ZhOKirdSzIevRIrXTvRA6UUKV9KVRBHniR1kATWH4vLKss77EcbogVVa4qbQHWqBnoMtI/0Y02ylvcRy6okvvWff0Y3s7RzixvB6z8b0OMf1HRzqdi0l0rTZ5rib59KlZobdB9pD9OBRow7FLn3VYc6bUhHHT4PuDLSTWVvVMeSGPpS/tuXwzMeO0l+CooLRTXcl/EbyZK6k7gigiuEpummg5X0WCqKkMmBO/umIyqx3kqfg/y+cOBg2sjX54eoXlw4wYpdEg1mZTiYb3CzwOah3Lt6XwwV5YUjuxWjg8uF3upLTbNnUF5x8Ms4uI3kMwUL64+lEuciw24Hv36vyG4Hj3sr1YvphmfloD5Wgt5zRXtnItSOVeje4w7HZLctIRVaq7kzZvztBr2sCIkmv/XYrUP5SLuf4FNNu2dpTkdki57q4wO5d/NE9JKDMfmbinYvoT7N8bkVbaWVakR7MUeoYYpvduveOfl+x/pOJHdpJoJ+mpBf1Kwe5ujWsf1gjCsM68cjCIFoFf1UUHa7lzD5+wV9qelnuThOEkVIBMkoJ3Y53QfSpSPZeJrjDBUixVVHdbcgW8ih4ApNvW/kQQwe52zlCZnZle83f3KP4iJiG0HeaGlsKd+0O642XQ1cXxuZjRvmDxZszsZMf5Fi1gYaQzcP+FK88sqDLzTtfkKwitkXi51y/9a9EhJJ+CGBbh7IbyL5TWB04VAR6qME3cH2jqa8FHG32ZMStS8VozeObiKI043jrpFkfc/gx4E48oRUNltIIFkY3IGT99sP9POAaRTt3sBTErlzvBDb4Va49KfX+4ySFt8aQlAka727J9kysPjEUp5JBbC9Hxm9jvgCbn6yh8uVoJqRYfTlOc1JMTgGPDc/GLP/q5rqJCMqmLxsmTxvGb1qBLGOIumFRTcKd16irlPu/B+K0RcZ6jbBjYL4tTVEr+G4JX2dopxY99o9RVJFtqeJ+M2V2tnlQiKJVA0NWNHC9GtFN4+gI2prSK1HmYjZGJK1ot5mtG9K3NRTvjJsj43EyluAFWHymxXbu1BcBXwxHHTrSLp0ZLe90Co1lK8MtlKM/q4QSqd7l4BGX9/ivncf00VGv3jF6KzDbjyuMCgP+UWN3fSsPhAdotukpAtNulCDT3ywYbbSRGfrgN0olILYauwWRq8l7pK1OMWSjYj0/YgdYre1ot2LFBeRfhYg90y+SkToPmnZ3EuJBuoToWp8pminmr1fC7rXPdw7WBJmbtcgKA16Cj93GB2ISjzo6TpQXgaSrac+NDSHst/90HSnYhwEzsj2jpYmOQfj53JQl68bse+eNiLu3vqdlhDtEAvryOa+0E9hEGX7iSKpAtVJgs+GHJMJbVmf5Iy+WWCbQHrbUt8ZsfrxMVEruomhmyVCNW48tvG7ONddGO6not63+ETRnGSY5ZZ06dBtpD5O8blh+6CEAKZyu96e6n6JacN/MK9+J5K7clEU4kLjxinz32yp7pUka2nwGb2saQ8L0psOU0s3qhk8rVEr2j1LcSblTTu3mDZIx5hRJKseu3VoF8ivO6JSJBcbxl8vaeeWfmQoLjuKa/GNzp45QSpfXBG14uRvaupDCa5+LKguuxUUbWtoD1PC0D03eeUYvfGM33hW2xxrAthIdTfi5w7daEwjaDnYtxY4R7L25LeB5Q/ngt6dlM+TF+3Qgjpspo3eldE+E4F2+vWa8auO6XNPfSCC0ugi7NwFphu8vx3MvpLNGRIYnQfSK0NykewOkmgl0FVlhO+tFMlS088C41eB4ipw2xQoQJcO04JqNEeTLZVLOTldYL8Ys/+laCNykKh3CENLg0lUcPjLnuLKkW4CuvGYJuBO57hcQrKbW/b+fsvi44Lxt1tsHdmeZpjGsb1fiJe7VruOwjjtiQcdZ38sFrtQema/NjTHkfKZhUYTtpZ+LvfjrYNl+mRLupEGlbe0Q1IJRYOG6RPh20H80rpT5Jcau9Vs6xRjA9FEsVxuLNmdCrPVbB870nXk+P8OpCs5+LOFI2QWn79zMk2fOZoDTX2cEBJNPzYiXEYwrRJU3IvOki09IVVsP9pD9wFTe7Y/ukc7T2j3LNGK6NYeFoRUHmp0mnJec/D54F7KFeVrASkgyN1nim4W8a0BG+mn4Eo1eK6F3ls9yiiuA6YV0bObiWde9yI+qk6RvkpZf+KwawUXGaaXWE6XGjeC/NqTbgPdRJGsB/+5Dphby+RbOWS0l+e5d7Tm9YsD7EqE+2bP4AqJ+7cd1vmtxw8NTKM3PdkioLzoE9rJoVwfSVUrH1Y6q7NbQfrFuRbUnAulur2nKEIg5qQAACAASURBVN9ETC3Pq5sqRmcB5WDv726Yfr0R4TQX27TPNNWjGfl5zfZByfjzM8bPtiKeukj51RXNUUqwCp8ONOJfv0T1nvQ3r9Euis6l5NDt78yJVtEcGIrzVpw0TcDnmn6SYprA6E1PunC40uy6s3/X+k4k92gU3dwy/s0SU0snomlkw+ku0B7Ise5GlmTj6KYSuH6Ska5FNe6nCT5VTJ6IEOFzI2XwLKU9FBdOdZzSl4rtx3u4aU66lu42l4sg001EcW/2FNX3DqgPE5Yf5YzehAH9SnJ8S7+kK2luKa/ECrU5NbRzTXVg0DpydT0hmzWCRpaGUASCgfZeJwk/l2CtThPqAz1YyoYGjwPF6mHO5KkmvRErmq1F1Z89aXb2OF+m9BNLVPIz2wj36IbRDcvHKela3DL9ZHA35NJ40t3t6Y96krUEv+4VbuJJFxpbidMgGkiW4vFv5wqjA28uZ4TO0B55mDgeTm54s5pidMD9YMvyw3ejFNq5Ep/86N3zVgEWHyY0+wa79dhFhW08/TgRqxuiIdSnObaN3P5gTDuXamD5yYjioqN4uaY4i/R7nvELjU4CsTWEPLD+UA6f6o4Ixf2PhOrJ9xvsfgPjnn4iVMftZ2NME8hWEm+jc4mJZBOZPnO0c6FXQjo0bfXyjxsF+k2K2ybENOILOTTaSugqXcnWWnxsdjHjU8368QjtYPlYfr65YzFtZPysftdeP4wsCCYSEuGog4X6wIjtzoC5WIguUTl8pjB9RPkoZoJlJ77qVUQlgeb5hKsfybPU7h1H/vZZdGNxZdFL5VK+lvecPnekm8j4tcO0kepI6EefSVPa+IUcUj6F7EbTTwPlc4svI6EQsd+PAvX9nn4c6WaGZqY5+HxLNwVbR2Zpjd93rB+JrhKMgIzNNkdlop0YMZWQbAOmDWS3nnY/srkrlYppI8mikesDVo8l/nyidlXv5mEhyF0LNeWzd3x1yKR6srXoK3tf9zsrcXWs6aaa1Wd7rB+PdhTL9tRiq0CzbwRJd5Hu0YFUnonCp4r+dMbkFxc7mja9bah/cIfmpKD68QMBR1H2bX5Wsbmf40rpVA+ZaEzJymFrAWvd1OALyW2uNBSX/zh6/06MHwgWqkNDN96TeTDNgDJSK0lCA0rEHxWkMWL1KME2ya4sc0WCaSO3P5jgCkmEyutd8wWI4h6NojoytLNix69uhw3mMuGYfS7VRD+SAKiPNOVFQHkoz+T91w8U/QhUMLhCFHbdC8qxVUSpiE08xgTaqSM7S4haeMwmJMPBhSA2Kx18ycYT7krnqK2Eutnej/g0sv+5dPqFVLH4JMd0Ymfc3ssIRvjK6bOObmbxmWLv31/y5i9OpClrrIfuQhGD5d4qmbOxNiKA7v2WaBihnw4zX94KwEPC28tr7n2w5G+/+BB0RKeev3n5iPv7C55d7uGdptxAdUcx/40XTv1Y7w6maMUaOH7tSNc9tx/n8PhQDqVEDc9SfOPtVAuKHjZws69JV5Hl4wx7J2V7H4rXln4MxgRi/dbnr4gqp3/UEt5kBKfBDjHlNeY8w+eR8QuYPG/px5Z2JodHX4oD4vZ7lpAaJi8dy8eW4kza/+tjcYUQFabw+HXC4eMbrr7ZpzzZ0rUJLgng9a7S62ZSZWzvWNKN0DT5jWgk0Qp6Xn1UCHgYK4rrQHGhpRt6HBm/kW7avnxrvVVc/av70oVZ5pSXjs0dK30UAbYnsq1DAknmiHccyc/GwNB1O1CNPmXnuDj5d4rzP9HEscOVSpL5scWn0I0s4zOh+UKmqE4jMZFxDclG9md936F6RfVRRzLqoEpppwnJYnjuGtqp7MXlRyUhE9Hzye0B9iohv2KokOHkbzyL3+uwo8DqfJ90JY6X5QeW0VkQerCXwypdCvpdfm8iFtkk4saR7EoNFYiAHdsE8IrgldBcvcRbspafrx+pwUeuWD9MdrqWK8UyqwehuS/Z5ZPqSGYT9YWmLxWbO9L6a1r5ndvvF9gP8mF8AzQHlmAU+a2n2Zfek8kLx/bUcvPDKfnSU+8LFTwOQp2qaLH1uzwZFQLJI0xeun80r34nkrt2ws2WZx31ifjxisuOfmwpny7ZfDJn8vMzFv/iDqaLZDc9xBTTR7LbnuUHOZOXnQz76oWX7OaW7NZhKkdMNfa2Rl/csvzTD3YDe4orTz+SGRHpyuNKTb2nsRVs7ySCchLp0EzXntvvy7QkW0G2hNEb8b7Xh4Z2Dska9n5V8/LflDSrnHJWU78ZS2OQiiTrYbBQZOhghPkvFlSPp2yPzSAIIQq+Y3BbaHHnJJFkDfOvewBcKbSM8hE3kW7Ldj/ZoferPzmmvAr4FNItbI2ifK12YwXSdYReYe5WhK8EVodMxhH4dBhM5MUKaYfrrk4V+1nFps9IZi3eSQkzKRv2sgqO4OnZIa6Uw06FKGIkkrCzpQRouglDyZySrQLZ0hMVtHNJUNpBdisIfvVIgnv0qqEflcy/XNIdlHRzS36tWX8g1JeJCma9lN2fJ7QHsP+/ZWwfKHoN6ZnF3Y6Jxx1uKq8dzizNYTJs/kh9qGjmmm4sM3WaA4VpDQdfdrz+05RsAeWbIfF91pEXHdVtys2vDlAnLSFo0qzHbROyOxV8OabdH6iNEo5+3pJebLn5/gHNvnQaRwPzJ6KLFJc9KiY086EFPxfbquk85csKYqTby0kXHck4oZsMFefUkC8DphYkma8jPhUjQHedo0qPHeyHuoWDrxy2CvQjI6MDbgLXv2dg2pC8znaHK1FQerqO4kq79lyfWA6+iKzvy7C8ZC3xZDZaGsUOWsyvxsRSGoV8KoehrdTAcyuWj0XzCAnkVp59vhB7ZnWkufqhpb4ZYS8TMJH6WDN6HUk2Mu/F9Io1htEbTzOXCjW/lf0LMHkKk+c9lz9NxO4YoqD4WpEd9oS02Ll2NvclaZsG8quIL4SaUVGq5+m3nupYc/JvX7P4Q/GiJ9tAshZqJBp5/enTluYwl3lKz9dsPpzK4DEg2rc2T0VSDTlHi025OTCUl36wgEf2P99Q3ylINo7T/+WK9Q+PCIli9rML1j88GgRkGVuw6yr/XXn1P2KO/v+9tJM2cJnSFynOWsymI1k5Fr+/j2kj65+cisMkUdjbmvzG0ReaqBTZOrC9k9BPDN3M7lwQ3dQSraabJlQPp2z/4CEA5VmHbWSao+nEzuQzRXWkmT9pBRVvpXPU9MKlA4xeRtQgMikvSFJFQfimBTdSXPxB+Y5PLRvG91fEJNAeeUIa6SZiDWsOpcxf/WCO3Xqihb3Pl4P/VhL0W7ueWOkEYS0+SrC1xzSR4qyRRoYglsO3CKkbiVrfl4r8ZrBwDnSMrYVv7cZKvPeXBc2B4uhnkb0vwU09xaUkeGk3V8y+loPGNnDTlnz5+pTxqCHUlnCdcn+y4O+vjilsT1G2uFJ42dVDwQ7FpXDwuh9GOUw1/UhoqNHrlmZP6LZkK+K3+JINbqTJF4Latvdyps9alp/NRId5uhkSh8y3ia8KkhcpxZOU7T21m64Ykog2nu7I4w57YmVQvXThtjMBEckm4BNFspVqqLwSiiZbRGZPW3QfyG7F5jo6E/dGrCx1lZKdVNhKESqLtZ7qcoTeGtwTmelTnCvGzwZBfGZZf28+uIHetcb7TDF+0b6brjhM7tz7UpLO5tRw+Z9NaE5L+qmlm6Wkl/VQJQZ8IvGGli5QWweyhdvRPLEV8fktorz51HL5k5RurLGVPGdfyJgGNxl4by0OlWwhe1P3kXrPyPXc02S3keJcGgfbvUiy0bipxzeWqOHe/+5wmWLyrSa7HvSpmfi2i4shxuvIPK/hbkN9IEPBps8d7aFYFPd+eLWbHqqdjMxI1l46sDOpJvf+vibZBjZ3RWtTnWJ7V/HmTy39WIb1FddiPjCNor4tBFgcykC+kEXqE9EQtvcU9dHbxjWYf+PoS83sSc/6RyekK0+ylWvb3EshMlR8is3DUmglq9h+MJEDpdDSNe4io2835NcyFmX6ZCsNaVaAgis1o2832MrTHBeiq2UGvzciXYkZJBYp+U1Hfuspzhr6QtGP/vHs/p1I7oK4A/WRJbvu6fZS6rsj7G3N6FWDcgHdRkbPN2Q3juqDqYzVHBqWiJLcmrmMOm337eBecfRjS3bVysiCXCiHt1MlfaZ2fKXLNZOXjsVHGa6QU3b0soYIy8fCs9XHEqQyslbKODV4k9+6X8ZvRIBLxx3LbcH6zQTdyAiBZKUYvYL2IOw6TJOteOqVh9Wns50Q9HayY7IVcaedaWka2si8lXamWX5cSmflUH/1Y8X020YCKx0alepBVyhh+q2MFQ2GnW9ft+IIufhDJShm1tFNIF1p8itFeiO9B9LUAyfFmj969IzlqmT+dwn2uOaqHnNnuiLVju2ryeBzlw7FaGUjEWH6bS+UxMIPgR24/bRg/KKmOUxxhcJ0gkrSjfxOO5PnU1z1rB9kTL/ZShv/QU5IoDjT6FYxfqEIGTQnfjd0a/ET6Ubsb3JJ6E6RHjTENJBcW2ZPAu08oZtoZl9XuyYa04SdlbQfSbd0uowsPtIsPpLPkl1YwtYSgqJ71KJ6TWI8yawlTAQMaBfppm8b5SQ+3h7e49eBZDNQTk0kebOQ//+ipT5SdHuB659IM5v2MLrwu+mMtvGsP5mIoDhMFWynkmQI0M4M/djIcDIdSWat0DpTpCILcPDLnr0vV7hS0Y+kOkyKnqgjuoXiJhDVMA4jVTvB2WfSvZutI82hYnsfDn8hw/GyC4OqDO1pz8t/bWWC5L04jIkQmyGwEzh9prjYjOF1PjThGTZ3Ze8WswbnhU5bf+SlEhkp6sMEX4gxobhy3H5W0OzpHa0XbaS4EGouu1X4IuIzzehlQ3jYMD3ayLyj134YWCbXpDzkN9IctfeVGBmqA0N56ejHg8aXa4JVdFNNUkfauREHkxHr7t7nK6Y/e0M7k/2svIjg7dxw86Mp2zsJ64c568cjytfNDkTaOtDcLVEhki57Rt8sMI2XXFUa0kXH6tMZ/Vi0tW6ekd/6/6Bj5juR3N/O6M6WgfTbS3QfpRHpZER9mtHuJ6ChPSppDixmmPVu20h+XpGuHGgJyPEXl5SvG7GWvVlTvNmKELtyA8qRg8RnGtsEGf8L2DbQTaQLNVjY+/kt5rZC+8jp/7nA5aKylxdhaIuPQ6v64P/1EvhXv6+IJsJXI0Z5h5l1Qq1sJHE0h4r0VuOzOMxmV3RjPcxv9zuxqx8LN+4K6SpVITJ70pNUUTi+kVi09n5VoZwka93D+mEunvlGxsPaRSsz50fCEfZjhEfPpFRWXsrQw59L52hwmuZxS3YNm4eBbi5NPS5TjF9ERqbjy6sTxuOG9gD6ZcayzolR8bNnD7CH9W5+dXMgDUzaRabPAq//LGH5iQhzPoPqyDJ53mGXDSFRFFc97TxBhUiy7PGJYv/Lhmam2Z6mMhNnlooQXRjcKFLdFbGz3RcfOkENziLF7BfJjqPU+538TEeyswTVw+a+pi+lRF59WOzmg29PLMWVVBvdVItGs6+YPQ2UZ1J5xaHrtbvNGf0yhwi312PcTQ5eKJX51x3ZQoZqmQGpV0cGN45sT/Xu3mQ3LdvPjqhOE1Yf5OTX8h6mVhQXir1f1dIh3cnkzvRn3wiNNTPk1z31oWH/C+kZcINmkN/0uyFxSkE0kWQ12AYD3Pwg4exPZxRXgaSKtPsRd1WgvKKfyCEUjVAHbw8628jfrY+UiLAplG9kJktxpmUOe+nl/kahWnwZht4MAUCbu2YHSJJNpOkS/Nyxfigur24qgOLe3pIscehGUb4QHvqtRVj5YZZN//b7FyCpAuULEc77idBg0QxD0gDTenxj2FZiq60PzKB5Kfx+Txjm5vSlgM3ReWDv68EM0Ubyq25nt84WnvFXS/IbR7qWnNLMDdXDEc2HR3JdY2m6ejvwK196pt/UpJtAsvGsPizoxorNXcv21OwcZcoH1p/uCQPRC12z+rCQ97rp8LkmO68EMDT/BJJ7SNQwelfTfXhEdWR3DQh9Id1ZyoMr5IQuvr4ipBpbB9Yfihe+vPD0heLyz05ZPyqESy0Sto/GcqNcQPuI9mKXBMjPW6JV+ETRDg4csQl6Vt+fU324R3Wief2v5yS1zJ1YPRbnyNsvA2iOxAr5NpjyG0ma4aOaq+dzQq/xuYiiuhf/cEikWagcLItJJQdGettJI9bw5QbypSFCp/hUcfHThGZf0ZeK2ZOOZOPZPigYnUsQ2EaqCFcqNncN3VSz/GyC6eKu3LdbEdJ8OvDvJ510vc60TKysjVjaPgyMXmmyG8XqA83+lxuqE8VNV5Jaj1bymZJZy0f7V7xZT/j03jlKQXMSuPe/rjDD9UcFlz8VUUl3isWnkdmTjr0vN3Qzy+2P99Au0s4TUENX6zhBu8jqg3zXUm6GmMive3yqGL0YxOFxoLnfMXmiUQct2S3Up4F2b0hmpSPcppAGmuuCfvLOqhmsIl94TD/MdlcyMmF7IhtOvsQhUt0J3H5fS6PYNOLKiMo9dtrhM7GpEhR2v0FXhoOfKy5/mkkVtxJ+tBtrDv/2lvxS+NW3nZz1ST50YMp/b+/L+AE3iqw+Dtx+VkCE+sjSTTTrf/N9XC73szpJmXzb0M0z+lKhvXwG5SLNAejCYT4fY+p3rf2mEZG3vBDuWIUoX/4ydqh5R0gj46+XECG/Gdxog+NHRakA3fDlL5sHglCT9SDIZ56QRHz+9htY3n2xzOxJJ/Pcc0V1RxJ5MXQ7T57JpMb9X/U0xzKn5+Lvj/CjsPtOAe2gPOuHL9hQbO6n5LdRnFxTw+KziRxmDrJbmf8UEnld1fbsvnFkqF5CAtuHXowBrdCt5Xlk9ShFdxHVBVypGX9+JrTgz8+HAXCaxY/ky4VcKTnJ9PKlKdXJYJC4dZRP5R5qL8xAyM1uYOD+//xEKvNK6DL5ciJDdSffGTm2j8bC538jimrIDcna0ZyWg1Hkn0KHqlJsH5TCf15V5Iu3VEWkuHLDLBlJnumqZ/v9ox3aL656spuW1SO7S3BvT8eQ2ndUzESoFeGzFNnCsXlUoHvhFEevO4pLCYD1PUN51uJKmRsdUmgnhmQtQWMa2Zi2lpnP6SaSLqA9iKw/lEFHEdi7vyQfdfgy0k+FougmavdtS8sPxeKY3fTo/v+h7s16bcuy/K7fnKtfuz99d/sm2oyIbKrDLqjGyEa2BG/Aiy2EVA/wBfgIPPOCBAJhHkAyCMkIU2BTyFiqzpVVmZUZmRGRcaO53enP2d3aq59z8jDW3jcxyqzCllH6SKG4d99zzzl3r7XGHOM//o3QtZx+g7OXE021bWm3GqGcdYrA9MaweBBSbvtUI8Vq3yPf1fLfnsfwhVioxjfiFd/0RHHqFyKA0qbz6mjAdX7uOMjvN6jYUG8bdKXI7hmKQ+k8zn91INatxqcX1rRWYyNo8hDrNMNY+GrDXontt/zkb/cE8qplFxHfqE0Cz/E/tswfhMyf9qQjLBx+JlNLfFltRl6/ct0+xlENZSGXHcvCfZ0epPZLdK8Bz7E6cXAZkd0Vh0L5RPBPI1wkToUqlVZu9EweZt3Be3VPhFp+KSKwNee92JXDPFh0C2zrGH4JXq1wK592FmIjgVqwivYqxsYSSjF4IbsR68kB3TttuPnmhGqrY2I5oes1qSzplJXCHF8qdClGXOFMvn9+IJ+zXuAJFCid/+Lh2stE0rjaVNP0xQbC8y3124UwwDoo0PpyyBY7muwkpBxromkHy8xDnILphxP80rE8EfuHIBfvJxMKo6gZyAERZOJXXxzIfW1yH/04Q01qcLD1Z/LM+YVY6jb9NylPo69bWuPhpS3NQFLTJFENNI7jdy4IxiXZ3c6uwRfGSdvThNM1MUAUrU26XtQ7+q8tvXND/6WwF/zSUdwZ4MctzknxdwqakcOlBkqPZuioJnJNxFxNU0+kccvf2hMYOI1Jz6UBC1aWcCbTZZuISnW9Oxy8qvBXLcu3J5vJ3CnZuciexLD81x4AdIiFIfrRS5qeJ03LWbOZ0rzCMnsrpR76tLFHPfYJFw3RtHkjjvsZH78Qxd3LRcXV9HyyJ6MNB7XcDogvc6LbhnBW468sq4NIsK+gi+RTChP7jL4SUyKvdvi5Ib4qKfdESLGOIlPGUfc0QW4xoYzjJtKi+toXOKBJ5SFfHUUEmSFYwfiZwfqyUFsvqdpUYYKOo9+XG7v3UrH7JwKxmCxgej2gPOux9q9oe4Jbq1YeLq+SLnTxIH7DOzbCVEguu2VhC+FFINhtR3nMDryO1y5MhrXQxKsc6ZWlmGhGX7csTwLh9AYyuq5vBmUh39XUBw00ujNYAm/poW5DvFwTzRTxhSiEmoHIz3UDP7neY1lFZLcp/RcK5VvOVwOWZURlfK4vhiRfhfReiMjCBl0ndesYfQ795+LjMfms3CzI0zPpTEygxMK5e/jXTBuJIxTrYr905Hsh1UCLe1/p46YhKvexoUMZhemLd3zbd1QTS7PVsv3HEoeHFSGSCWQSaBJ5U9bv9ZopMngtO4kgk65254cGv/OnN6Fg28QWIkuQySLSW3gwblBGMXuiWR16GyuD+MaxvBOSXrdEt110Y2dLHK4sTV/Te1VI8fNh+CXEV1qmPK/zre9pemcV1cAjnplNJGXvvBHcPJd73wYKEwvDRHsWTiVNrO3JYr2edBF4oQS+xFNLdt+gPu2LHUWuNpGITitJOyvdm/hFYPvHLel5F1mXdz5Ckxa/19C+6MFNRNNXLB51P3vnD9Q7tVTbkJ6LDUYa1ZhMlrDWU/Laqea66BF4hvYqYfiFHOZeLfeLMnJf6bpzbL0SLryuHLrQXQMlh6CX684XycO9ToiTmqbfLU0Hlvh5CJGosHunQnOWhDMRD/mVpdz2pRl8MmJ1JLsh6yuyE0kMSy5rYa3FQlXOjkOq7ZBqqFncD0luDMMvVuhGoNJyJ9hMXkFuZUfy9HhjfliN/Y2lc5t63aEqX1sZmD1OMLFHPfhXYKFq0kCoh42lGniUY8FC21hRHPWEm34YU+z6m7GznHgdI0KzvBsyf+BvmCLZccjVN/sb1kz6ckU58cj3fDlVuy6xGnXwxUBRbmmqcUCbyoPYpLozD4P5fQ8bwupQ0fQFS1xnUbaJYI/KQDSzLO8qin2Ligx+3NL/2sNNGhEGraQzrLfNhqebH4Q0qXTSbcd7Fk8W6fJNz248PPyVknQcX2x8hUkgLBblpDuqhop6rFie+DgPjv/hNTak41sLPc7vHAnRoBLTTR0WfZxjxw2mb/BzWU7iibf48sTDJGCtwjmFFxtmHzbEaY2vLe/tnjMIKnYP5qLW7HJDVSv/L7dleZ0fKFbHiunTmDYW9eDiQSqQ2TpZRnUOoaEIUpZ3dHdQyAPrV5bkxojD5GWArjTOc9jOG8ebVFR3K+JLhUsMOm2Zvutw2zWu7KwC9hXX31zzwr1Nis9aV7Ha91At9M9ERXv7ttdBJnKI+iuF8ixUGhNBctrhwpkPRrxzgE12bHZXcNfVnk89cpvsTt3KDqWNFIsHKaOvG/yVLCvXP0t848Qyd6JYHUb0zsTOV7JpFdfvh11nbzCx13X4UOwr6jLAu7uiGRms/yYsxgZQbTkWD8Ius1Ph3s5w44bdP2/EIbELh1gzr7JDj3Ah0M7lt32ZilbytfJjy+DTgCBsMandPA/hQiwWlBPf/NWh5Mu2qdy7adCg05Y2FQuA0RcNqzuWwDNUrY+LLF4Nq329eU5l8Q7pZdvpIjpjsqGHahVNF6oDb/yD+i8rzE6N71lc0C13+w3VtiXodgTFnjzfTaqwHfFg3eUv7oaUE7lO1Uh8rNZwJ8DgeSl7t04ZXg80YSbfvEk1q7tp57kvB4NXyzOyuOsR3xqaoU/vohUVsi9ZDuWW5B/7ue0sMRS9FxnpZcvy2Cc7/vnl+xeiuCvrhEERyJLUqzsVnacotjzC738hPhrnTffmWYJCYJlg1bLzJ7d4pZyc8cwQT2XTXI00dV9T7YqrXXrVisR74G8M+sOFcLr93DF7IjdvtSUFu+5L51Rtuc1o2fYc5Ui6RmXl5NWN3KzFTmcIFournWk0y6eNUMw6D+1mIJ7XfiYS7Pi2JVrIBRS7WcEEnQfx1BLMPPyVpASJmx4b0VKYyc/VxpJVWg873L1T5ekWph9t0X/dUm1JdF+5o7oIPOk+437VZUNqmkVEOixByTLVBQ5VK+7+7y3B0jF4YWlqn9tZD+0ZVGCpXvSJ/Ja8DWmd5vpqSDhTJBcypWyusYPsrpOs0XMZlcOlZfgsE6fEWUWYSYfSe1V0EJzr/LoFV3WdOhKE5x8sO0Oq4wKda5x24vT3KkFPxeNExQZ1EYkCcxmgWiWL2IeCLYeZLBSBTWj1uqgFK5kM/cJx+EclyaUYovXPDG3P4Z9FIo9/P+vEU5rRpz7hVBoPEcrA4qnAXMsjv+O8O6LpG2hl8mfXEtiSSRdXj0UclB9ZYSwthZoZ38rPWewGm4B11wneohuxspBFuQRaVGNH8DLCWYWyCq9WmLiztPU6D/YaVgfd9HYb4xzdbkd3hy80XRC9NgJHBhmEMzEMi2ZWjLgCx+qupf66z+Boid2tQXXujX1H01fk+z42FH93P5ei2Qtqkl6N9UURa0OFiywX8wGLMkKn4nPUPzN4tWPwUpaYAPXIw8/dhjq6bg6avvybRAfSTWQ9Hx1Yli+HpKdvDNSUg3Zo2f6BY/vjhvTKbgK2tRE6bdNTJFMj+76pIcil2YlvJLov3w+pJyHJhRjcNT1htvkrS++sJVwKPGdCoV57ldQqp2Dve2WnaJXo0HooC+cmFTirnHhUY2kclIHsfp9ix+8grJ9fV38hirv1FE2iKXY86n4nRFIiTBg/K1j+5ttic3kQkF401ENPDLs8xfRJTH5/yDqhKN8Tl7zJpyW9M3nzV/vyZphYU275FDtCzI2zVwAAIABJREFUG1vfJEHmGL4UcZCJxTDI+l3XciVquHJLOpBoKh1ociE3UjUUG9P4WhgbEjUmsm/54g5qTTM2LB7bjt8rVLc2gWLHJ1xY6cQUXaBGR7f0lYhYSkW5I1aoXsEmRFg3oj4V+1Yt0Xtbcknj687HeqAot+TQMqml/6Lz9W7BSwxKOfJDB+8tQTua2kcnLboVD5z0VPPqtwKqLcXsscY5hS092sonTGv0QcnL6zGrNuTz37/PaLISG+AnECzkvV2rCMMuq7L/2ggVVMHyYZ9i2yN72Ge175FctywepBRbgj96pXR0ybWTZWAN5djrFscOs91gjYLdqhNeiR2zVyuKpxUsAuIHSzkwU4O/1LTHFTqSguu0QAvFthS4cluRH8r1LnYVs0c+yjqWJxHFruwN5g9EbBVOFQowL3rk75ZUBw3zd1qakaUeCXS3eChy+vKuLBO3fpzj1ZLDa6Iuwi6N6L0uhXJo5BBeHYuAafm0odySn23rh3NMqBj/aMbwRUv/ldDporlEPpa7If1Pb7vUJJkC1ZNMJq1MbxxLm0HHefdESSwmaY5wq4TKI5rJz+48WSYHmcCSOJlWg8LS9KHaM9x8oFjedwRTjd4rCZaa5dkAV3rgJFwmuRQFdrEjAsD4srMZ8BQfPz+iLMIOJhKqIcrRNh6Pt65RCvJ9RbGlpYv2hLFSj6DY0lQTzc73M9FwlNKQxFfy6LWpQhu1YRTZLKB/Z0G5LctgrNj+Os8xf6iJLwvS05LehXhamVDhr+zGI8l0kJBuJdijHsl4oJwU4esPEvxcdifRrMWrLCbR2FCCf6qRNKy689zxS5g+jahGonC9/uUduZ4L0dj0X1cMXtbSBH1ZCG06VkRLQ/91vTFb+1kfvxDFXbduoxjVjYzkTkvn3PRFTo/rxqP7IU2iyA8jvEoM6/2V/L1oZuifysJsdRxRjX1Gz3L6r2vqviZYtOjG0evyS7MjbzP+37wTEl+5LkpMLkK5pTGJYHsg2Gk1ls6tHiuGz5uuc5bi24wsZiRQhis9XKOJvw7Bt/gz6dajW6ifFNjOWTJcWvI9D+crsfhs5YCJb2VX0AwsxaOacs9gAxg9bzvPa8XyjtfxhzsYpBE/emXkoS12hbtfbkn6D4Fj+q78W2wAptakUUO9Y7BWOpm29vBfxDRbLS41rO4beq/FFlhZGA5yVOkRPo+oiwAzjRj1hQ4x+eYVs/MBzWGNXygW77QUe7Jo6r8UquXkMyML4D39ZgrL7UYlWk28DZOnTYRfH91Kp5+eS25m/1W9cQ/0pp3UPgvAt7jIwnZFe1SJO2SjKMsAb6XxbgIJOQksfiiisjaWnUk1Vhs/nHVoibISRnH7dsDVL1uKfYniK7e7pfe2o+1bzLDFFT7DHweMf+R31FdYPmkxA0n/oZXl/OxpKra5kXyffFezeDqgOIjpv8gl/KERYzpzUoJRm8N4+t4Qr3Is3h6z2vdZPJCc3HIiB3u+q5l/sE0zEF9yp5DD2Cps3IVZHxtM7MgeGqqHFfXY4a8calJT5wH+XKDIaiyUx+UdTbEtu47epbDWbt4XY7loL98waJq7Fep5Qv24kN2OkUXi9Xcs+aHrJPzizVSPbKcncPSHBWYlZmrTJ11nu/Bpa495nZD2StqPMrE42BPDwGqoKXcNNpL38eqbfYKVE6g2cBuv+WYA1ncs7obEt0aWq9oy+kqmZD+Whg5fDrt6K+b23ZRiWwRRJpTGSDmpRWEm7LYmlXvVXxkGX61oEoELBy+NWD84Ogad7MbqnjSVayGi8wRyrQeyM4vmIpbsnzXydX5wKTVlO9woW2/fTcV8TAnMU24F/2osVIU/ikiwr9tNWG7TU8wfBh1TRm7ynT+dERTyUJZjj6aniJ5dCKY89Fjt+SyPfcqxuMVldxOKvYA2geXdkOzYE4MhJ0VxdSDYqnKyYFt3KLp2bH0ioRc2hORSOvP0TDF7LDmMs4diA2piCQ0GNn4tqhJpfrVn5MHuiX1uPep8UAJL/5Vl9sjv2AtiruUV8hAu74o9qq6VbPl9R7BUzO+JyMMvHOMv5HCphhp/JQ9QfL32sobeqaXue539rkJ1rpTOQ+xTk4aiFqgijWu82wDXauptg3/rE1wGxOfCry53uh2D1QS7BfW2xXV5qXXr8dX5DlXjg+8IT0Oad3KGn4h1wOwtzfWvGKptgb6KPZmCmlQxvy883yaVIhvNTKdmFeGOV0tBXReH5MqyeBB2dMmO4ln4+KMa5Ts5SF/EMBUFoVcofN9gY3n/bOhwZzFbwxXxlUwA8UygtXKHTgPgaAZOincqAc2qUdiom/RWkkCla1CNIhxXbH/Xozh0ZCfCnLI+RJcSHsJ5RPLalxG/L/eESdwGAnAepKcF07f7zB4GtHs1wbemeIFB9doNNU4srSXbtE3lEC8nnrgvNvLnxbam2NZUI83wCwgCgzqPZCK4X+Nvl6iDEgYNftxgfWGJqMsI5TnakaHekqAQG71xz/QqURzbQBxL64NW3tfdmnZgcJU8U8Nhwe6dKf6oxobSpUv0nfyM8Y10acpIE5XNE/Ad1Y4huXJcftvHDAyjcU7ktWTzhPZ1ikmk658+DQgzi1fIYemUTLImXKuxhUJajxyTz+QQcZ4s471Bg+9Zzn5bGGBNHqISCVavR47FXTEftJ7kk+pW6kybSK5yPdACySbSGJY7Adff7NP0Rd28PJFnrekpih1h71VDsUdQlo4PL+aE8VSayiYR6KocK7KjAF07Ln/jgKBwZAeevNZZqjQ9MURLLxpZGP98a5lfkOLe4UnaQDXxNmKBTdJLIDe3Vzvm74xoEkXvtGHr4wVeDdO/eodoKdJgpyGZWnoXluTGSuFs5AGN5lZwvUC+F8hm3MTSJUoOqVyIaO6Y3xc/ajHWUoRTYRz0XwslzPnSeY6/MB1+KT+nXwiUghFoQ7Wy2FOtcKSbaUR45ZOdaE7+x+edH7XwrpuBPLg2dPiFKBT9wOBnHq67Ub3aEWaO+b2AaGnoXZguQYjNMqlNxLK03JIHqXfqiG48YRU0MHuiMa1H03gEuwXzl6NNMo1KW2zkaLZb6rG8j37xZkk46hfoUoFvRdLfXcf58xE0GnO/xHuWkH2n2HjFA8TXarPMCxdSVIcvW8l7ddA/F3+QdWhGNfRoEpka2kSsWZUTDH74ZYFugN0KVWraVQCZT/QiohlbRp9p6l/KhIZa+QRTcS3Ec4T3MozVRLeScFRsC7WvGXRSfv3GmlgUjgoXOrZ+oKmHjuxRs9m/2MRST2PqvzWTLr5nKXfkug2/gOJe0x3ebLz4/Xm31N0RhotuRZwVZha/kMMxez0kiRu0LxOe7JC6JKdCoBinZa8UrByrI8XweSvpXqVQM+uxYnXZI3qwxIxagn5NeyXRblhFM49IT8VKILrRBFGL6mLx1nF5XiWeQvme3ihUp+86gmufbJbAXKai4EpgxfLPtrh6MaHNfTnIJraDyQSjVxaCTBEukOf7OiT5IiS8lYSl+BrCa4+q8fnJ6T7qNiTIFMFCKLHR7KfC4X2oJ45yWwru8o6PMgpdKcJZtxB1nRnbrcO2Gq0kurAeKcJT8Z7xZx7JhewXBqfiLb+GsPxKdi/LOz7Wg95FS3rV+dMHitFXDf0zQ+/liq1PK9IOfYinhnogO8TkqtnERXqVY/C6FYFT2e3QfBHeufXS9NxIpmtHW65GemM4V40En6/7arOD+VkfvxDF3euWo8llTXredLxay+Bly/DrWhaNviLMLOHSsPUjiaq6fX8o6fHTVrikXYiwV4ik10RyQHi1iAzWyUbJ7boDcxve+3opulbAZSea4cumU45K99IMpGhefyh0u6YvAc6X39TU+y3tpCV5JU567a4EQnsHOXsPb/BvAnSlGH8KetjQpg4s3PzGHZG5p9LVhXO5iPVhI2NgbAmDlrZvpPhE3SJNwf4fz6mGHvVAMLtqrNj9finWC3NZvJpwzUiB8kgEIqbD9QHclz3qaUy4l7P9PU06KXCVjLfxqwDTtyzverQplDuONGwYRBVH718wnOSEpwHL2x52GuICETXZm5DgG3O4ijp+r7we33RZpKFj8diRHXsbv/d4aqh7Gq9zBO2fGdoYoqWVTE0lyy0xjUNorqXCDwyMGnrbOWpcU+8Ywt2c5V8p8L/fF9sB2HhfR9cexXUq8W6+3BvJjcUGjt0/lczNetuQ3Rd4RNfCoHChZfXXM5rtFjxH9lGJCxzJTi4Ycesx/IknVhO5LDCvf6uC7uAu9sXZsY2lcNYjWTK20Zvs2mBlCAqHe5hz8viSNKpJeyXhzFEPxTgr35Hl5pr2Gy4knCKainxfN+KPM3hVU48chJYoaIlfhjSzGG+nwvcNOpCbqE1kKs4f1kRRg78v/x5/JaWhHrvO1leRXIkXy/b3Fc1WS5g2xMcZO99n4yBa7rcb7541nBlNNdmxQhuBetYuh37hsLs15b6heVgwf9dQ7MrXqYoAswwIlopqvyU/Ulz9qqHpSePX9qz45eSKcuuNxYPrDkMbwOypphmaDaNlMsmoWw+zJXCMbhSu9DYK53BpKceemOp1QqdyojvF91pboah7urMNlp2ZU+DdLKm2AsJZw/B5LbRFK1Tb1aFYXNRDsfpoeh75jtf5NUmurtcI1m4isRi2gZgZiu5FGo1gZbEdJTLMJCT85338QhR35ymihSTJrA4DooUU+/kDn+wkJLmWqKly7IkS7e1+F2wgXWq+F3S+7B1VaeJ1vF8Ze7Mjj3DWkl4KB7hJhAkwepYL5cu88enWlRTxaOqoRrKlluVqxzOPZEHkZzIWZsc+8Y0cIlgon5biZR0avHFNL6m5vBzRjgz9d6Zcf8fgWo0LHKOvO4+VbqwOl3LB2tQx3smoxhrda9gbZOih3JDKyvexgeL6W0PqvmL8edEp3Ry370R4lUwpa0c/E8riL3kR4OWKatvQDEFpR/T2HJW2mNZj9haSLlRqGDaUDyp00QkwJlIML6+GNMajH1YkYYNJHJSaR++c8usffYq1mugwx3x3jHeUU+7KQeoHhuU9TXoulqy9F/oNja1bSI1/vEQ5R9nh3yZSLO4JV7xJBXdeuzhWI4/i0DAZ5PSGJTv9FTb3cb6lbXzcZSQT2p0V4RcJ7Uh2FuVBy/iHPq3xKA5k7F7teXiV4upbUiCCqUd86VHtt9jdWuyjI0M5i/FnPt40gKnoATzPonst6k+HLB9Yeq8U+eNaTLWuQgafBUQ3GtfBH0Kj1NIpltJdVyPN6lCsEsqxwr5OuFr0aY1H5IsgLblQm8BxvxS3zfhG1KXOe9OgmEjRe56xuBfSDi06MEzPh5h3MgYHS/QXCQCm7BTZIWR3xLYhX8U0M+nsdSV7LqGNWqotMZvTleL6Vww692hKn+I65eLXDfVBQ3tQMzhacvftCzDdoRg5ikMJRm/j9XQugjMTKCaTDBdbuIlIXsl7bh4UKA0H925oH5V4C5/03JG+EKjEq2UKNancS+FCmGVrCb/1xY8nXMjvi325zknQMj0bQikHZHnYMj5cyFJVC92y6SvyA91BK5qj/+1cptal7aamN2hAmwiTxnmK2S8dbrJN15Ts9e5IWHWC60czQzRrCVfyjBbbMhEFuTSw6YWw19KLhnIiO8FoKTqDaiTwq4k01VBtlL8/6+MXoribUPjmQdYK3ztWNIkUJjFf8jpWg3TaXiVZgso4rCeYt+74qCYRXCuatSTnJdVQNvWrw5B8z8Mr7cboq5pElGNN79wQLl33dTuqV+a6HYCMT9HMdfmRsHyroT2ssKF0meWug0aCk3sfi0mVXQaY3GdVhLhKo9KW2W2P8MZDBxYv18weexJq3T30bQyjLy0mcswuRRGa9iuKJsAPZAnc9F2HC8uho5zj+oOEfO+N4MdEimosI79wgoV5AV1EIN1BdRNRfjYi/DoijBqRjTuFGtcELyNU7sNOJTz5SjH6HPZ2F1in+Oz1PnXrwWHJzt0ZgTZ87/yEOKmpigD97Tn6Jz0JrqgUde0TLGXEBzlATSgdqNMwfF4xe2/QxcAJrVRZ2Ppxg0nofE3kmnu1ExHSSnO7SFHK0VqNXnkMPwnwgxavUNRPCtoyoNxric99gkwK1uwbLfNlQntUEU8dh//ovDN/krzPtmepR1Ygiql47zujmewvMD1hV+x8T9GOWrLrHuosJn9aYRPL8rFBFR7Zv7FC14rluzXV0wJlZaoSmwO1WX6HM9eFO0M18YmnDnVUMu7n9MIapboFoWZDy/QK4T1XW75c26JTH8+EMpndlwWjt9JEiSgZ1Rc9stdD3JMV1SKCRqIXhY6r8Hotnm9QsaH3WhhYQQb5yTq8RZ5Vv4DhZ13OgtHgWxGGxYbtnSXLiz7zv3+ECizDr61MLqkhuVQCL7VCBXVeF/5uPFRoSV+LGjt97sNZTJJWeMrhbAdXpuLXoxyUY0152ApskskhUo3ECle1Mm1toMBzX+ikmSWvA1SjIehM22JDUQUQyeQW5AIXhnO3sdyef7QrRnyRNCFeYWjTzr53Jfcqzm3CWKwPJvEk5GblGL5siW+N2BlYR3bsU+yIvUo50Uw+r/FzaUDrvtCTZb8RiofVyhLOBFwXQz23sWMJF/+CsIxS6r9WSl0qpT7+qde2lFL/SCn1eff/Sfe6Ukr9Z0qpZ0qpHyilvvWXKe4KYUVkdyJM2BH9A/EYcVpt7EDLLdkW10PF/H7QKdakqLeJfKG9f7okXDpu3ouYP0rF9a4z4VqPUX4lb0q+54tp0ljgGr/sMiJtl7noOiVq3HHJz7s0ntzDO49wXVefXCjSl8LaqL+dodpuLF362NcpTx+foS9DgouQ3qkwTkwqHW056eh+210H8ETETsGgEtwtbHg4umbYK9F7Jc3Yyr8pVYy+ajfLqnApdDgxVZLlYLGjNmracq+lOGklmi60WN/RO1liT0qahyX5LMEOW4ZxRRC1NMc1elLBjQQKoyRVqW49brMUPzDMXozxfUPZ+MRew6OtGz7YP+Vkf0rx9QD31gp/qal2DHHcSOeZddLvQRdGsieTQbErS9J6IBL1QYfFX/xSgGqFuzx80dL0pNMZPq9FpLU952i4wAHeYS5B0toRvr3ALgOcUbJ8i90mEBwLbeXjCllmN0cjiYyrFcWeE6pe93t2KlZHDlrFKCnxt0tsz7C826ldu/xUjCJ97m/cJ92LnkCC8zeRCVcfxZLes2slsq+FclfR9mQCrXuK8fevcWcxh70FB70FO+mK8qhhdSz3S76rcX43jjvZGaxhy8U9ee9EIwL2qEQpx8HJLTxesX1vimk8hjsrWST2WgkL6Um4dxAYnIPVkcNfCVPIX+qNajo/lDjBxXs1NnF4kSGdFKhey8PDa66vBkSTksUjS9KvWDzQ6FKjQyO2HKXAPPgOMzS0KSyu+uzsLsgetthQHCRtIKwW4xTOaO7+2iuy7xSy5B4pehcGIiPaBS0L8CCD6RMPF8oyeHUsjpyyM4Hs0GP2Yszk3hSd+SzvgR83RGFLPKzwasXivqjYszuKetyxpfqaaigYd7GlOf+VZFO0pJ4o8n0RQlYjqVfLE1Fni/DLl7yHkTRb1pO6lu9qqokI0KqJ6jykFFcfJawOZIIoJ4rpk4DlnVA+fywOnasDqVtrgdbP+vjLdO7/DfA3/pnX/hPg95xzT4Df634P8G8BT7r/fgf4z/8SX1867m6TLLJfRbQQ7Myr3/iaRzNHOVEd/1nGUxHuCPvD+oqbb/RpE9j980IyEM8sk08Kgtzi5zJOlWMtCwwNqyNNsaNY3lWsDhXphSxYi4OIcNWFBGeOtifKShM7bL8leLSkGRnimfDEi3dKXGqwX/cwWw0qko7HbDX85LMjbOJojyqW94Se1r+zID8yeLVQwlTH+z7+JyXNtsAkXg2t0VinaY1me5zhQks9kDFueSyFIzuWm0G8UMS90l9J7mY4d4Qz6fbDa49626AqwYWN0Tw8vMZmAR8+fomftpxejFEKlGeJooa9t65YvCNKwHrsmJ4NpZtciZd5dZtQVT63ZY/z1YAXywkvX23z13/9+7SnKdaH0aceWluyB4Zi3+ECy8EfWSY/EdaCX7mNhcOa1jm/LwusYCX/Wd8xfRpQTYRVM307EoaU8bjM+szzhPYipdwztK1mkJRE154U4A5iKA9b/JVm+LlPMijxlh5+6bj6IGHyYyge1pi+oXqrQNUKMzA4p2i2pKP9+us92quY+CzAJI74zBe4Zd8QXgQUR4ZwIfeWDWWpqSvFaJTjxk33usLFhnosk6LthCheLdPU9Fs7RPeX/OjsEI1jUcUENz7hVJMfOaKFCN2SGyNKyVIO73Wot7/qmBWpwrWaYVoS+y3OKvIqYDxeiTOiVZAJBRHlyG9TtLYMJjleLVCGv5LimN9tmX2jRRtxqtz6pwF6UjEc5OSLGM+3PL+aoBTc2Z7xzreek9+mYlWgHLbRBHPN6m4rU/a4xpvLJBePS/IqZPIDj+3va7Z/JNhKYzxWVYgODS/+8IT9fxAx+qzDubc12rcb7r6yYlcdTR1oYTj5K4E3m11Zfic3FgYNh4MlbtzgFRIIrpWjukgp71VyDbSinojVRNNXBIUcOPFUPKSCTK5bNBVqbzRz7H4vp3duRDtSO7Y/LiXtqWUjwtMG/JUhuZX9X5C/acpMDL1zCSvpn4o31ejrliCD+NYSFB3T6NaiuikhXApc9/M+/sLi7pz7J8DtP/Pyvw383e7Xfxf4d37q9f/WyccfAWOl1OFf9D28SkZUr3as9nzJrhyKH/g63g2luuWX4/D/vGLtHphcC8xS98XLPSgkC3H2KBbual8cBk0kHhleLSe5brs3PHeESykgft75mFTif7J27msGivhGio5XKPzbgObZALRwiZudFpYBGMXehxdEr0JYBJSHLSr3xa9jq8R/HeHVME4L8s/HxFdrJ0pJfIlvLWe/FqMLj16/pNhTTF+NmNUJRRUwyxK8zNsYkKGEsbN+sE23NKwm6ygxgTJMDGhHfdRISHcuY7dSTvDz/YxnNzs44DuPnrM/WrK9laEULIsYlbQEc+FeD/cz0qjBu5VwClUrrNUsyojpMuXsesRge8Xv/skH6EqRnongxBjdmWHJDfn6twQ+apNO6h2Ij7YJ5c+TW0c9ViSXjnqA+OBPZCcSLgXGaHuOy1vZAewOMrlfQ0db+1x8uYOJIZkUkunpg6qFPrZ8bMjnEmtXDQUiWd5TDH4UEkxKuI5QexJw6WqNt/TwfMt4b4muhGUT3UrnzaCVgyAUZ8hyTwRgyd2lQDwDS/nH2yj/zX2K91OK5744EtLZR5Tbmqb2+fD4NS+WE1ZVSLNlZAooREhT97VEsNnOH2cA+b4UHFFxVkQLh3cRUtYBWjmUdtRVQOgbhv2CuF/jAkt65oimIhzqRTVlGQgLKBQqqJ8r+s98gluPcAbOk0WwmYUss4R0WNIuA5pZTJjWPPvygB//+T3wZd+FUwQvI+Jb1UFLYOYB8Y00aOV1wuomZfFIIMTVgfgZrWYJvrbYq1gO+wd6A5naULG9lW2WvjZy7P6godyR7xHdaJqBQD/BdUDTg/6LgntHNwCoaYDXQL2IBFocyD6rHjlu3+/U2gNHci2YeLiQ6yYujjLh1wNBBJyGqw9TCSJB6srybkR62RItTSeslD/L9wIhBCA7pGosE/j4c7OphfVAd+ZuYqRmIlmmbn2SC+15pEmmFl1botm/HLbMvnPurPv1ObDf/foYePlTn/eqe+3/9aGU+h2l1HeVUt+tTU7USc3roWL2UIRLTdJhk7EYPRXbmnKimH24LYKIgUA21VDeaOuJMKDpyYm582dzdAu3bwVUQxmXRCEmnU1y1VKN5fBwnnTDa/+YegCL+x7pue3G344KORBMPHl7BoEjO9H4U5+d+7cMD5acf7onmGLgIBB8dnx/RnsV04wNo8/h9fVYxC1V120VjmJbc/1BNxkkhkFcScblVkmoW3aGKzxPMMXJZ8KaKXcU0VR8RKwnh1K4lAOqTWF5X5ZCJlZEkxI999F7Je2W2AlUVUA/rMifDxmlBfY2YlqlvP7eIVdnI5rGw9N2Y5OKgqb1uPlyQv9FRysrNXHcEAUtzUVCFDcspynffP8rtj+4Ij+2NCOHtRrdCDWOwBHeepRbmvEXbfcewOJe1IWosPHvLrdkMls+hP4rwS3Xfj5eqXDA0XBBYzxheoRW9hOhZfLeNeazATbskqAqTTOWa6JDs2E/KAvjZ5bi2zn2dYraFzhjsJ9t/OHteUzgG/RJzvKB8Kfzu9IR48AMOlxZyc9VPRtSj7rF3wcZUdxQ7HT0vECWf/NH0tlnd4XhgoLx5zVt4/GjiwN2kpXQSwNLcdwSzcX5MLlu0UagSBPIgTX5zG2w4Jt3Y1Ew31+hlGNRxuyPl/zrD59Rtx5J2DBISwb7GdP3rFBMfUdrPEb9Eu8gx/lduPjIkj2Wa2R9UaU2AxgcLbm/f0P5fMBgP2P3zhTzVR+99PAKhRd1UYaho95vqQegWqEbKqMody3VliLeKVCBJZwJ3LY67nx7tIRkM2po9prOW1+eVT93GCvXbW2jcP1+QDQFPxZ4p5202J7B3i3Y+dhw+06Kc4q3BhfofWGUoR35NGGylYmBnicL5N5L/h+Zr9VYVMtAtx8qqSaSYWrCN8Hj9VCaFRMqsuM3gUBNb23TXYglsKdIr203da1pvh1jL5FJrB4IYUMZqVUirhJ4uu5p0FL0f97Hv/BC1Tnn2HjF/X/6e/+Fc+47zrnv+HGPfE/4zaOvWw7/YLk5leLOy8FEivGXjYzlvTeYo1c5epdWPCbCNUbeZWIe9ogWlt65gFO6cRRbnZXq0nHzjQDni7y5jWF11IUILLsxWUO+rwkyiVfLj9yGQVDXPt7UZ/DC4hWKq9MxWjnuv39KuFDE577w3NOW7OMtgv2cDBw+AAAgAElEQVSCwTOf62862spH57qDHQQrryaK5ErGXqziatbn4I8r1Bc95nVCazVFFuEXiulTTTSTqaF/WhNNZVRrO8/t/FD+veG8o3uuG4OdCmsU4YVPsa+IoobK+By8fcnNHx+gxjW3ecLJt06JRxVx1HBvMsVZ8TqpRyILH9xdMH+7xTrJZXUOLi7GBPsFxU3C0eGUH/7hYwZRJaETuWJrsKI9FiaAKjXJlVy7YtsTrUEXlpBe1B2HWfjt5Z5MdboRfHNty+rn0A4koPrzj09Igoa2lvc8CFsePrjg6myEepKhjGLrh2BTS3iric98bCOWEOvcgOlbmvCHqQRxXMa0i5D8ixHBjQ8a9t654up0TDON0LUcLv7Mg1rjdmq8TLN4r8YbNtRDWSQmV4rqYYnWjvK0JxBT4Yh7Ncoocd68liWuiYT+u7wTsL2V8Zt3nzEMC97/8DmDH4d4mdd5ComdRjSzko41kII5e6KphyKgiaeikdDaMb0esJXkvPxql//riyfM5j2yMqJsfMoiRDddOPurkOPBnKIOaFYhjBrKHUe4l0Ng6b9QZA9bensrypOG3f6Ks/mQ/sM5q2XMbJkwfu+G4GRFu9dg2y7B61JI+jZwRNce/ReyCO+91BJN2YiSe70/0w0bzxTrBDpKn4UyfTqhMRe7Ck/LhK5rNorfpg/tIsSGEF76BMMaBZz9mhTuynh8nu3RZgFtDPGwYrCzYrmKUZHAKvXIUewLT97EqpsWXEeysKRXLZcfJSRXjuWJj43YeMUEK/FLavpq4yPf9BXhogu8STxxg7SSU2FDNuiCV0ltWRM71kpzEwtsGS5F9LZ+LTsKyE7+5VAhL9ZwS/f/y+7118Cdn/q8k+61n/vhFGx9WhPNLfmux8UvD0ivhP642hdvj+TG0Caa3us3jJlg5eifdxFcI0V8awU+0eLDfftu0DlMKpquu0chfg2ZKET9bkPe9p2oSa0sjrY+aUiu3EYtWWyLJ3p85eEe5dRlQHiryfc06ZkD5Zi/GvHyu92gYpHNvJVuvLlIpHidadJPItS+FLr4xhAs5eZZK87Caw9rxe2y3m+Y5gkXp2OCuMXEju0fC4UyyODmvXhjgqZrOfn9XP6sTcQrxV852sbHlj6qSxBSjWD/jfG4+OE+0YdTbBbwZOua89kQrS114/OjF4f4VwHOg+RS0ZQ+i7MB3spjdTogP5Qi0hsVOAf+zOfih/sM3rnly7MdVNf5FnWAug4JZwILLR5L59L0FdVA4DWceG2A/Nz1EIbP5N/mZ4pwCb0zx/B5LcK0Wxnh3/7gBUUToJRDVZoyD3n1R8ckX4WEYYvzHLOna1hGICv/MiR57ZNedVTNLimp/1yuQXzqC13TQDBXXHy+wzeeviR96WP2a5q+xUYOf+HBNCR9PGfwSYi7jAQyHBmaFPrDguo8JTnzSC+E615cp5j4ja3C6EspWG0sVMybryb87mfvcl32Kdpgw4MPF2/cBvN9j+UdTf/UdNm5jslnhuRGoARtHGHQ8u0nX5M1IeGkZH97jms1y1lKvhLK4+Ar8VnStaJ1mlFaoDIfdRuKC+l1wv2/B8tHIgCrfzIk6Nd8+fWe7FG+GuFmIXHccP1qTF0IPLn9jyOZLvoOWo1uFSf/Ry5LfycHufWhzQK8hUf2qOkKtRLFq5PC4HxH+W4hXW4PZk888YZvJc3LBeJoKl034Dn8TOC/9jbG8w3hQg7PNGiojYfOPNJLeQ7a1sO0HmQBXqmIbkXFXI9kIWu9LkweKLa8LkOis8vIHV4hjefO//ITovX1WUozKOLMjuwwVCzuhURLR7iyYjFyIwQNE4kGQLeSL2y6g8Kr5QBrO6ZO2+l2kmvxpfKan19X/3mL+/8M/J3u138H+Ps/9frf7lgzvwrMfwq++dk/hBXmyuKe37mfOVb7vvDOzVpF6LE88ajG0jWtWTVV52msq7XngtpQhHpnwkrwS0d8JW+4biG8zpk/8Gl6XeHZVXiVEqaDEmrY1UcB9UAKY/9MLElN3CXk3MZoX+hoQdbJt3MPFxua/Vowy8CRvPLwLkOGj2a41FAct6zuG9pvL7E30o1kJz7jZxW6cZuYNxuAmYXC0JkUbPdyGfc9S7BQzO97lLuOaiKfX42F0eM8SC8cyirKXYGaeqeCXdtWEZ77OKPov4LkylHMY85nA6KHCxbTFJTj1XJMELTEYUMQtOxuLzH7YlLUJpK04y89ksdzekdLTGIpi5DsNsU0HtvfuGL41i3Zj7YIwpZqIoKdVSHe1/VIotfGn4joavRVI5auB1rCT2rElM1JF7V4KEylti/2DMrC7EnY6RIUvb0Vn3x1xOn5BNt40G/pD0rq/ZbJr5+zPB9ImMNITKWakSV/UjN5/5pyX5LtkxsrB2PqmH7DEV9q6ollddfSf9nRbActz/7hQw5++xXxs4h+x9M3qUXXIqPP7hlc6LCjFi/zGP6VS6qPx7h+S35HIMDsRBOMy40que4yYsNOeVnsKU6eXvLW8QWv5yPyJqDctQRLCYIXu2vxMO+/6sR/V47+q24Z3e1fqoGm+ZMJpQm4/PN9nIPTr3fQgUX5FufERyg/FMy+PGhpjMc0S9E7lfjzvL8iPvd5/RsB8Z0lOvcI5or2MmHnYEGRR2w9uSU59fC1Bd+itGP/eMq9/+BzVseWdmhJXvq0iePyO6nsu+ouq7YCVQuW3fsqIJ6uQ9k1yZeROI+2iv6fJF2oDWJX3TVBg+fivWRSS3wt7B4VGtElNHLgVdcJxb2aYOV4dTOmbAPx8w/Fc6epfTzfQCt5q8WhoU0cD/+HqQTL/HefszqSMtn0FMs7fufbDuu4ysFrw9m/+xbLEy0UXiNQS+9CfpZiR5NeGnqXRpgyO6KzyQ9UN5U6cAKr9S4Nw69brKckGFvJc11sKdLrlqYvOcN+zkac9TPr6l9UeJVS/z3wh8BbSqlXSqn/EPhPgX9TKfU58Ne63wP8r8CXwDPgvwT+o7/o6wMbxoAoBRX9c9NBLJ2vRZfd6BfCCfZzseXVjaPYFWxeCj6Mn7VEc0v/1FCONdsfVxQ7Mh7VfcE8bz4a4xSkl4bd7xUbiwF/JRdY0tw7zHdbcfVhwOEfFJieoRkaekdL9PNERsmwGyM1xM9D9CzARI5615A/rnEnBeX3trh/74r0hY+qFG3robZqgqXcHNO3IvE7jzsOsO/oHawYvpS7eFYkDLdX1FXQCUqcLGnLN37u6838/DFU25IPZyJHdqIotx1+1FIfN9jK4/YjQ3ptoVXUZz2qrwds7SxRieGoPyfwDLdXQ+Kg5fJyhPKlUwA5YN765a85Gi6YpAVqUqM9S9iveevkAl9bqsbn8a8+p5rF0omNDaN+gY1E5u4VmvkTmbzmD6UzVVbea1znq93rwhkiqHYs0Y16czM7edDqkSX0W3b2FvzVtz5nsLVCeZZRUvLek1ecXYw3CtVgrnGxPCxKOwLPSA6qk92KMkJp9QpFftISLISSmt0VgzAqD/vhkrPZkPLQsHjLYMYt0X4OR6W4IGrw5xo99wnnisXv74lGILAke7l0dKcW9UVPTMMOpMgt7nniNDqVSLnz6YBPPj8m9A3TLKX3SlOPLX5uiWeWZqA2asfsUJPvy75p+pYviUC1HBrtNzIusgF7H17QXifcf3SByXy0dni+wTUSjF1tCT3x1WxEP6kwpUf6g4Tw+z1hiy0U7SdDohstzqS7Jdcvxwz6BfOPt8VX37P8zQ9/yHi84uYHu/zo/JDhM40e1xSPK/xCrmmQdYWnkQU5DmzPkN8xrI4EstCVorhf016kHR1Z6oDrdB0AZS2whEkt+MJY8ypwVnXRhqB6Ld6oASNwnjWa2zwR3xwf0rim3ytpViEuEnGSn2nYq/jsd0Z4BZz+e09oesJCanud4Vsk1MXsjjyzIn5CiA2+NFvFjmb2UJLSTCSNZ77jScznljSc489lUZ5eiXlgPRS65eKeT7mjmD4NqUfyvdIry+yRTNDze8EmLvHnffxl2DL/vnPu0DkXOOdOnHP/lXPuxjn32865J865v+acu+0+1znn/mPn3CPn3Decc9/9yxR38Z7WZEcaP3fM7/n4uevk2rI4XcdfideyI75xjL8QrnPvsqV3Zml7itljn9Whx+KuhEtkx6GMTx23fS0t7p2Ld/ar30zQNRKDp6Uzj6ZyI7apbMZ7p456FEAiqtMkbNCPss7DReTULrKUdxpsbLH9zoEuaQkCQ3mv4sXHh9Tv57jQyQ14Gwqur8RBrv/a0X8lqT7Oh9U0YXniM0gqfM+wuO7BeSS+N511rvVl6gDovRZ+r3jBO+qJkQ4pEi8Pazt/69CiEsPlNzUqsrjUMHp6i/sH27jc53w1JPAs8aCiNZqd3QXbk4xqrxUptNF8fbvFO+NzPtg6ZTxesTvKaE9TsjrC15ZvHr7m0+eHeL2W6OGCdHfFTrrCzwT7bCct4VyzvP9GeGa9TrHZKe9cx4YxPcEZ8yNLcWjkIHBdt6PFyOyd7XPGQcH+IEOfxTRW89kf3UdNBTtGO+odI0uz6KfcACNLsf9G6bf8dokNHPGlT9uTbtlfiU+QP/OIwpZyFZIeZmDAT1qq85TJaMXTx2eoRtHsttjI8p2/+bH4AmWyHOwnFatDsbRtTiq8SpoR63W2yJnbcJk/OnnN/slUoKw/HVJNBIqcPxQZu+p2Rot7eoNPF/vS1ZpYgl+CpaPJQmaLlMZ4jO/OeH66zf6dqTiAOiX3ghPPH2/uU5UhV2cjolch2TvisZ4/bGg+WFHvtRT3a4bfvMFexOzcmbEqIppJSzuyPJ1c8f2bY/b7Gfa4xFq1oVkOJznWl39DudU5aUZSgPV2RTwpic+8rrGRJguAcS303ndKmqGj2BcYI1g5Ea4ljmDmkX4VyORtIYjbzioDgucRdhoK9m4Ej1cAvqMZgu9Zyjrg/t0rWYoPW9q+Jf1BQjDTm5rQOxPluK7p7EjEKyc9F6+b4Yt2k1KVXkoDuhbj9U8N6YUl3xf7gdWhJr6SDOF83yOadv7vldAsB69E37FmPq31N6sDr/OEF0i6GaiNy+Q/d3H//+ujTTuTn+6Htr4IAqrOR10bgT/WdpltApffjkiuxCagHghrIFw40gvbKTUljcaGEM8Ensg7iXF2rFncF2fBZtD5x19L51YPO2rkSlgzxa50Rf5liKk8mt/dxf/TAePPzSZoN/0qIDoNUGmLqqVwtquABzs3PL57iR207G0tiPZzticZrm+oJjJK2kCK2uK+iLRcIAdD01NcXg+5mfdQuScYby4mSl4p/jDhXP7+4r7uIu0UfubhFcK3Vk23mMl9dGTELXHp43wIX4SCUf69bdq/MSPdXfFodI1SjjhsSMOGnXRFazTxuU/vtdxMh6MFv/fiKZ8t9phO+0K12y9J/Ibnz/b4/R885W+9/wMArNVUZcir+Yj2oKZ+UjDYlfYtWHZitXh9/bt0G0+EKemp7panCn+l8VdS/MROVWH7Lb908JI/+PIRr/IxeRPQTlrmq4R20qJ2KwnoiAzJa5/k1MdfaJoiYFHEDLZX8sCGEj0XfRGLd//dulMc0qlzwasU9fcnvHvvjNV5D6/UmKsYHGT/dIeffHGI26pRtSaYeXzvf3qfgz8yOF943kUtXVe5o2AW0oxFgOOX0pUWO9KNmxh+cHrE7UJ45+7bC8zDEq9U1AOhuU4+byh2dBfQ3u1QDJs0o6Ynz43yHTuTJbNlQl6G+KHh8svt/5u6N42x7Dzv/H7ve9a7b7UvXb2Q3WyySTYXiaRWS7Ity9Y4E2fi8RiO4wQZ2BPESBwYgQPMBwHOzDgfJkYwmHzIYBwHmWAmizNWLFmyJFsLqYXiLrLZe1d1177cuvs9+3nz4TlV7QCRAgTOQL5f2M3qrq577jnP+zz/57+gtCGZOtjbHlFTCgbakIUW1kDiCu0DhzN/NsTbcbBuVGj+wEHZhqOtJrOXjjjabLLYGoKG5nuab994hFHocXt3jkeXDzg7cyzPYN/FtjKsqIBiQsH3rbC4vj0PY+TzrmwJbh6eFVMak2rKW6L6rm1AeVu0JklFEUdS0J2LQ8J5SSsLZ4SVldRzmYovBBjLkHZ9UemmitGwhHcoVrzdoxoLzSGDQL4O4Paswl5AaLxoaeD848LcbktYPioTpCHzFMeXHHShU4ia6lTdm9sUCW9ibHaK3c8rDp/2xIOqcIU0Wupa/xHn1BG3fJDTeT8k9TlV0Z9YLHtdWZr/qNePR3FXJzJs+XX7eiAjOtC6lT3EFvdFwHHi9FbfyBmvyGkYNRXV3YywrU63z/6RJNTYUxit6uI0LDryQE5W/0CgmNOH2TE4I/Gzjlqw+L2YmfdikRmvBiwt9uCnjsk/MOTwWbl89hTCywHZo1PsbQ/djFFWjlOLOJpW2Njv8MkrNzgaVAmPfQZjwTjSsqF0WAgZioSbzJfFn+OmlA9yLiwf4vsJ1FMZHR0YnRXKoxWJy6AzOklQL7peJXhmWhYOf9wwYBlm2yOeurqOqaZYoSI5H0KoKf17uxijsK2cW/1ZtDJ4ToqtcwaRz2xlQuZLAEeWaO79YJlxv8Td7VlarTGT2GG+PaThBbRX+1TmJnx981Gy0GK+MaJcCZmvjVHFniJ8v0lSE9Oryl5O6Ui6FqMKWqEnzppJDXR4ElYtXUxaFWy5/iDD6jm8vHGeX3z8DTrelP6kBIkiHHu0lwY4N0u4XY0JbMLFjHA2I+mkQrPrlZnca+BMoLqVEXXEpCmt57jbDmYmJq1Jd5h7ctiHcyk3Xl/DPbZIqxnYst/Iygb3wIahQ+2WeJ6Es4aD523sqcJEFkHgYk8kRMVfFhVzZUvSqoIZofhVdnJKh4Znl7dIej6TkU+aatS2j/XYiLglFhjDM7ZYb9iIVUJL5PPBvBHGR132Tia0KDsJs00J7Fid7bF2aY/Z1ohqe0q6LKHmGMgqOdrLqFwYkLRTnEdG3PttTe4Z4kbO8HyOveei/IzDmzP8xDPX5d4/tuk9l2B1HdqVKZeW9lk/7LAzrAu0NtUcbzeJOjlJXZbQ6WLMdK2gwLZDauWI+jNdjp/NsAJw9h3U1GJ+sU9wJWB8LmW8Ks+jSgWqK5Uj4qYsw61QoD9nJMwtZyiceJMpFs52MeWM6laGbkdoOyfqSDYyE5tB4BOnNn4nQPkZSSMvGHEQNaC8L8Z1SVVhBYZgTmIVM1+gFmcs92X5QMgcjXtpMaEoSl0J6rYLEZI7Nix/rUf7htgbo+TvJVWpafZUdDcnFhvTWc1kQfZL0zlNbUM0G3FdppTeY///sGX+Sl8nIQkS2WXYfaksI4d+mG8ZtWQBkXlywjnFGFvZKZZIRfBB5kknG7ZF1p3UFePVIpndlb+vi2SV8r4INZxhkcazlJ36neeeMGiGqw5HV1zGKwZjFGFiM7zbJDiS6D5jwXgtJ48t9N0SC8/t4ZdilIY8s9DKUK9N+Yt3L2NyhVVN8b2EUj2kdCDJ8eNVcYMM5uThNe2YOHIYrmm6kzIKBNO15cYzjhwGo7OSMTpe4zQ+LSuJSk/8NdRpGDBGEcQO26MGZIpwMSUfO1QWJjy4vsDPrF3n8uw+o9DDtTIsnfNc5wF/c+UdDsZV6vekY7C9FGshYHZuyNpilzB2iFMbSxl6UZnRuES4XuNXH32Vlx67y5XWLmdbPS7VD3DcFNdLqTzRwz8qMlXnNWGn+KxqUNtMxa0xEh+c3BVoI1pKBHNNBZsfrYiIZ7Y5JsptcqPQ2qBrCY3WhN56i6xsiGZFqm6NtNztWmAxy0+xlqbENfH1KG9ZRLMp5BDPp5ipLcvxZkrmGSYrGaqano7/3qGNbkXoUDF3dZ/sfIBqxgyfisnLGXNP7RPNZLh9sZgtlyOyUhGi0ithbLHpLR0YvJ5Mh1FLE8wq3j1YpL3Sp1yN0NpgtCy/M88wXRK19HRBkXsGZ6ipbGvi5QSdQut2KrFyZQXaUHMiXpzd4GNn73IwqtKdyH0bBi5+OS5CwIEczswf4zkpM8sDgoMy+XaZtJrTeKRH45EeSSuldMsj93LWhx0+NHuPfDlERbJg3+3VpagflkjTIhx8rPHaAbmXU32gSR8J8Coxdj0mLQst2LNTjnYb4GXEnxrQeLJ7mhOQjx1++rl3WXp+l/BcdBoyX/FiVK6Ib9fJz4jNpBXKTigtG4kY9FKutPeYX+hz9LQmGzm0GxOsTiTfY2nEi4v3+YnVOyw0hzAS51Z7Kt/LGQsV2uuJ71NWkufUKMHgw45YajtTSXJKqorhWVvcGrUox4O20B+bdzMm85qDF5uMF6xTj6Wo8VCXk7uyS5wsyr3tjsVuIakKG3D3wxXa70ljE8yr02njh71+LIq7sUT2GzU09fuJ2A046jQ4Y7Rs4/YN/rGwCuxpcTEKpaYzMdTuizy4fVPEKXZYeGh3DbNvpzTuZXgDU0iiRdnXf6wohkgIx8wbAouEHemKnOFD8Y5OwfUSHCunc6mL04hOxUNGw8JSj6UXdzj++iKek5DFcjPtb7X47Jlr2D2bLNVkkUWc2AQ9UUhWtoXuFLWLkGILMIorKzsYG3rdKq1yQG1hhOOnp+NlMGuoPZCOcu1LIV5PrmX1vnQumS+hBRj52Z2y7AoutQ9wazH1mzZYhg8sPeDXP/nnfOXBY7z6g0cIQ4eaG9EdVnije4Yv7z2BY2d0n0/JHUMaOuRGcXQkGZfRvTrT+3UGgc8o8sgyzbmr2/zB9Q/x2v01tqZN7h510CrHtnOZXDYbxLWHYc2lg4dmZsOzNs5UoJrSrsLtWSQ1URpmvhy4zkgWculcTG4Uh3GVUeqJqMrJGI5KVDaLQJZGDFnxFGQKUg2pxrYz7GtVodVVpVOrbtisfsVQuSce5WQKXZZOzNRSvLs+z/zETTJfxDlZaJP7ht43F7DvlMgjC6cSo/yMo9fnMZ6M8CgY7dXIHDmcnK6Nv28V+aSqcDqUSay6afCdlOH1DkoZFltD8tmYemeCMxJJvT01zPwgo3ZPng+3b7B6Nl5P0X3clhCRDHQ55Qf3l3nzeJXUaH7n8T/j7z76HVp+wPn5I7JMDrzxWemAg0TUrN3jKs9cWRe4wM3Rn28zfbuNSjTh5YD6bZs01/zRravkYwcVKSZDn5InYRg/88I7fPbCe0SdnPiRgLMzx2AZBldjdAH5VSuhGLmNbPrTEpXOFOvIxftand6wTL4S0n/QRIear33naQ6/sYS742KPpS5EiS1B242MPBH4LqkXBUUb5t/ISGKb726f5ejaLOUdhd8OiRKbufZQxEilkFl3xPXBPGFqQ13spNMKZCWxALCCwnwvgtm3wqLeiJNt5klBPrEEPsHNKZQ/7tAUDaZ42+TOw/jC8p5MWX+ZsadyqK9nMhVUJAnOaOnq3b58b2Wgd9HCCvh/VRfZP/rL/2ZexhIjf3sKh8+4p4uLtAx548RJ7mTxJdzg6bxwuaOW4GOZq047u+puTmjk5BuuaSaLEohd2ZEDImzLiWuPwB7JBzNdMEXijcHrK6xAPqy4ISNuOAvBYZlAyQa/uTRkmpQFJggVe+sdVKJ5/rM3uba/gJraGEcc8/6nlz/CBz98k9e+fxFvqNFXAiiEn8Gc3CTkUN009J6QT+xer01lx6CfCjmelkgSORy0+UtCj2LJvPOhEvX7OWlZM1kWPNDKpKikFYM7UISZxWBSYkO3SUKb+IkYq2/zjfcv8Y34Mktnj8jmNY6VMYo96pWQqhtxqbbPF7/4AvWu0CuzWNOeHdIbVKh5MQNtqJwdMB75pGWNyRR37i3wbz37FgBvHq2y1u5xrb/IdKcKpQx3z2L+tZSD523Ku0Zw6OJGNVpk6eWT4BGv8Nz3jQiD9sSGIrehdM8jbDs8Wdsmyh3evH8GteWDZ5hcCbF3PB79vZDbv+OTNjXkko6U1A3sVsUF05eHNa2K53ovtQlnctyextlyyT46IPQ9aq0pK2d3eePlS3LeZ+BtukRnIgLlSKGyZImJEohs5rs23asZqlCx2mFxLy/E2He8IrsUojY0b4rnyGRR8+LcJvnsNodhlUnqYgKbyVEDO4fKluxWRqviB64TRTBfwBX2SeCLXMs8tlDasPfKMvbHcn5v99MEU48Pnt3gtftrLM/0ebBfQkcaZcl+x/UT7C2PHxxewDRSyq2A7odLYmGtDenAJf7QiIO35vnwJ97jRn2Ow+M6neaYM/Ueb1w7z5cP69heinesCS2P6ayLSjSqHJP0PQaJhYk19cLvfTr0sb0UlkKCUQm9URLl6bkxUc9HRRr/yHB8JqMytEWBnct1QBt5kPyMtGyRZRrvWDNa1qg9zbTk4pyZEEyrpA8qZEsBw70apQSmkcvt8Rwtb0qS1Shd94lm8tPP6/iyjXdcuHcOYfvjPrkthIusKveAKpTCKhf2kzMuVOdTEeKlFU4ZP0kNUIaF76X0LzinSVcny9ikUhTu4rqotLB68GU/ldTlHsotMOW/Airkv4mXToof2oXa/RyvZ4o0eem+vYHg4To1pxF8pUPZrAt7RLbW9kSWDCdGWsGMqOCsiNO0+WBWFXmjUvjipik4t4q4KSNXOJMTLBpG5+T7J2WxgcXLeeKxTT569QaDzQaVLeHS5o6hujhm7tEjNv/po5Q9CerAMqhYodsRr716kYXLB0RnIqbHZcobjljZTuV9pjXB0hq3FCawON86JphRLDRGXJndE965l0pISCPHHUg3YYVQe5AzOCfdS1Y2hTAL4rpBZYqkavD8mCwVDxi/EmN3HVkENwJ+7aVXqLkR8Y06/d06YWqjlWF/XOPVw7Msf2ibcFZCLOxqQpppzswds1Ltc+7qNrbOaTYnBBOPmZkRn7n6Ll+4eYUv3Xmcuhdy491VpolDY1WinPQTQzY/LXazUesh59kqlotGKeKmYnw2Oz3AhCEjMFPpWJhRwVJKzY/4/NbTfP7BU3Pn1VUAACAASURBVKzM9nAfGdK8cCyRcWXD7V9tkg0kxxYtrChmInhsTLwWychdlgfGHssSunVNETdzRhcymmXJuz0R/WQVUZ/GMxmZZ9BOTuv8MUktF0Wyk6NCi+zyhP4lKO1Z6LkQ79ASpeO0WKqtJVihTJb2RKEzsaCIWoavvvYUL//ZU7z7YIlxLPSg+qM9YYbt5nJAlOTaORMJ1Hb7hXzdly7P78phaKYW+qkBNSfkxaX7/NqT3+Xp+hZPrmyTG4WppeS+FDRtGYE16jm1R/qUNlyijRrOgYPa9UmnNqqUka5XWfvgFt948zL792bIU8WL8xu8dX+VtfMHmFyRjN3T4JvDYVWeBwWl2amwtuwi0rKe0GyPxaXz0IMnR6gLk1PGjUo01TNDzGePqWzYEqbSMJTchLSZ4e3bOKUEZ09iFV0vFY3CiiFrpZQXx8QDT8znOgm5UTzx2KbYZ2cWJSvhKKhyPCkzfSRGRzIdTVZzOu+lBLPCZIvrUL8n1tnKFBBQ+JdSxo4N3nEBLxdTdFoRzcbJnyvvCQTcu+Sg04K9NxDixom3lR1IbQIJrHEmRpq8FMo7AuVVdk6My/4aYO4nMXcqF9bHZEUVQbqCe4VtefMnst7xiiacEcsAK+aUGVM6Nqe/lvQYTrmpJywCChigsSE5ljqTr8cN8Rw5SexRaeHR4svPZ/ycs6uHjGOPN3dXefbpu/SeEXzY62rG+1W6783y+H/6HmFio1pC49IzEb925XvMXT5k9/Ysuu9glVP0B/uypEUc+Jo35HSP6wpVSXkwaGJsqDkR9wYyomtt6LwhDnvhTE7vslA6j6+ICtYOhCOc1GFwJcGZKEoH8j7yXNNuTHhs5oB2dYo7EOvf8EGNP1p/mvO1LtUrx1x8dIc4tej2qzw7t8kT7V3u73eIZ1PcI4t07PD4zD77wxrXu3Pc3Zql36twvNXEdlPCxObPblzmP37qW/z6E6+wcdxm6eIha/Uek8Cl0goIBj7GFngB5ABv3EsEanNgfPbhvJm0RAiSVYpl7GLGwXMSWqCrCY6V8TNL73N1dpuyE2OMYjCs4F+TpbV9ZoIqp2gvQyUKo+U6xlOX8nWfpCLXLmoLvh/My+LY2CKT744q2AON46bceXWNz770JvbFEXY9hjMB7eaE8dsdTClH2QZtG/z5Cfb7FaqP9UiqhqzrkT0SMFlSQv80gBJYanhOHvrhmi4gAcMvf/g7rH14k0sr+6SZhXNsMw1dknrO0VPSqbsjiiD0HOMapmvpaYh5WhZMXg9tnHZIHDmkxiI1mpcPH2E3bhDnNkHiQGRRXbfISxmtxoQktvEXJvR366hnBrhrY6ypHBykGnvPpXqpx/qbK/zcB97Bn59gJjZffPk5Ztsjyk7Mb3zgm3zm6rvEjZzZJw7JUinQlWpIvF6j1giw3Ey0JV7KUn3Iv//Md7EnivxmlSSQh/Dc7DHe/JTwRhPzlQ5J5WFx9e0UPdUk50OSvTJppchLNZCXCjvcRGPpnNnlPpXtHLShVIoZRj5uH6p+xMa4TXdS5uLMASq0ikW+7Kz6j9gEyymDR4ScEMwVtGctEFBa4bQxiRoiZkp9Cjqy1JDJkhggqlyKfO5A6UDCt0+CgU68rIwFtc0Meyp7he7j4rFlRcIgi9oKty97m+nSw2yGH/ayPve5z/2VFen/r69/+I9+/3Mzj79Ecz0lqWmyknQ0fk/w4tyRhWdaBQzUN/JC1KCobeYEs1psP5fl4vu9AtuyESbOSLipuatIygCKUcERVkA8m6EKXNbvamE/DEQyrRCJs0o1YdXQLgdkaO7eWMYaWzTuAkoRzWeomYjdV1Yor43R32kQ12Up/Nb+Kj9z/jrvHy9gStKNxgMPFUmyusqVZKdWDcZWmEQCtfO+y64pg20IbrSIMhsdWtQ3DElZo3Ml5kaOmCflLuK8pwUTBDkgrUgR1Qy5hvsb84x6ZTnELAkw/rmX3uLVgzWmkcvegw5Xz26SWpq7vRkOgxrLrQHdYVUyLmcCdkcNkthmpd0nUhaun2IcoaFlmebi0iFf/dpzfHewRrs1wbNToszhaKtF1vUpL0zw3y5jCpMwJ4C0oombMo3krtz0dqBQsSatGuypxjtWOAON1xd1a9IwKDfjZ+ff47HSLl/Ze5yqHxGlNqGvaa0OGB+XIbQgFHdOK9LY+y56bBGei7GGcpMsvpoxvCDxeCqH+l1pMCJbU79tkU489KUx728uU3u5xLSlyRNNmNl42xLAbpQoeLU25H2X/H6Z/LEJqpSTTm1KuxZWqMhWYkxkYY8tdCoUyPqG4PM6Vbw5XaQ7qjC40SEoKRg7JLmIpFSmHnZslhwGIOrqqG2w4oKmd2SIOuKnk0U2V5c3OQqrvNRZx9Mpr+2vEaU29o2yUIFRTDKHSj0kfa/BzOUu0WsdrAce2dURWT2DkU3WSln4Q5fs0yP2wxqDnboI+BYnPDu/xYNRi2/ducS9/gy67zDEQR14RIFHul8iq2YoPyfpltCJJu1k7O+02PzX5whnhfaatCQg27EzjvYaPHJlm920RtrIxWl0T5GsxaQ7ZbLCOsEZa9yhIpnNcO97gCJrZGRbZcapS2bZpPUcy8242DlkfdqhMz/kJ+ZuU/VjXr13TjQXPthTTVbN8XpileEOFfYJawu5P0EaTmUKIaMjjBZnIl/TBUnECiGpK+KWKOqNFihW5+Kb5PYFulEZeEND/6Iit8UfKCsVJoe1h7ClolBM23Kvdr/5Z7uf+9zn/vv/p7r6Y9G5SwCHnFRRU8aPzFPC/W0oqls5waw55ZL2Lgv10Q7EMMlYRbBAKEyK8UqhCqsW9EoD45XCWKgmcuusZCjtKzLX4O3b2BOF19XEDUNWzmV8p1DIBjD7piEKXdYP23RvdWisDrBCGJ2BuAlW36byahn17IDucZXRxRQ0NFoTPn7xNv/6xtOy4Os6dDpjlJdT3TQPoQhLYIe0JPS6aOxRewDKEtqeAeyhLGV6l+QBtqeIuVhWPOSqgLGOETGVJcyayo7BxBZR6HL50hb4Gc5IY5yc4FzMt/YuUHYSKn7M6vlD3thapTcuc2V2j0noCu/9gYuKFbadUSuHeH7CrWsrTMY+o16ZbLeMZeUkQ48b769y+SP3+LsfeJmaF3F/p8P9gzZWI6F+vk8UOowvZMS1wjipJnRAdyjTC0aEQ7kFuW8wlZS0nTA5mxZcadmd6IFNuxxwP5rhn219lGYp4HhUYa3TQ6Wa/oMmzpGDKmdyqM5EpE1R/fpX+rjbLtVtQ+tmztEVG/+gCIH2DYOLhpl3jXipjwypb7DeqEGkWfqlDfGp8TLy2MJ56Zili4c49QilDFHg0H7ugLRmULcr5MUi98TcLU8LQdlJSPtIWE+5I5Po/FJfxDiLMXnPwz8UEZU9sFC5sLxat1O8npGkq1CRl0QAJ0lAck9l9QylDWsrR8S5zUq5z//43otMc5d2Zcpk4J8+f1aoKDcCRkcV4lZO991Z0rJhfDFGXavhXi/jHVnYhw5bn3AYrzc4U++hEk3rbQv1Rp1v3H4UpQxeJcbzYypbCmWZAmor6IBdi3DoQSXlxO+9PDuh/0lZsKYVg1+N6e/VGAQ+upRy694irWuK+g1R/iY1iYNMKwb3yDpluJ2YDSaNnLSSYw0s7PNj/GYoB+PUQinDq69dorSrqXshD4I23aiC5WRoV2wIANFDFCppdySdtE4eZvHa0+JehdNwa4zAv7kneHnuQOO+LOR1DNVtQ/NOjtcTxp49EjJIfV0W6nYgWpvGuqiQ3T7F8l4Kenm/MEgsFvOnhoA/5PXjUdwRjNg7FrZAMCcipdwR9d5oTdO5ZoSTvGnwj6QzyR0pvOLxUhTITHxTrEggCq8vI03pUCwy3Z54cdhTxXQpJy/nRIuJnLIJOBOFKos4Ka0WJmUxHD+uyVNFpzFh4fIB4xstVv88onlLhEJ5OSf80Jj4dh1yhdcW3qzWQht7amUb+0jMiQZjH33oyuicFJFe8UPPDHusUHZO5ijyyJKbToHbV6dYW2VHFj17L3ioTLjWcUPe6+jRDPfQkizXSPxKrEpCrRpw/foKbjmhtmGwKilnzxzyzMw2f3v5dcpOQtMPSEKb8zNdPCvlVy6+xsZRm7iRU93QjPslLJ2TZZrS0pg80Wgnx12ZoLUwBYybc/1b5/lXf/gpzteOWFvq8jcuvstHL9wRK+NjD/fIkvCUVKa03AO/Wxi5DQo8MxShErnC6tvYQ7EJiNoicPPPjJj1xzxfvscvLL7FOHbJc8Wt9QXZdRQJTCZV6IGNGciyM1xImd5oEi/HTBbFJlpnEgDuDhXescY/1Bxf1uS2LO8rO6KzaC4Nuba+hLENyjJ41YjB/QYHvRp5ZgmkkCv635snL8Ka7Q0fLPlsVAKVWojTtfEKEVpSlfvfDsST/+j9GfTtMt49D6NMEf2m8I8UXlfEekHHYjqvIFWkcwnWRBc5BVJs4oZi4esW5+a73N/usDut88X3rvD06hZRbrN93MByxUpYJzINNCsB9ZkJ7vxUUpwM1K67RJ2MYEXycu1AdCH1833e2Vyhtq7JXbl+L11Y5z85/w1++8pX+ez5awwv5KiuOE+iIW2ImdbswgDtSqatbWdYVo458GQHcaiINyvg5ozGJfKxA3ZOUhH6s2hRxDHSO9YkbfGL0nHh4ogsKd2eBNJEE5ewW0IlQDXlwkyXlz54g6htqNoRM96Y97aWOD/XhSNPGomK7GdOYNOTJKoTGxSdCB999UsDnLHs+7KSfL6TJYV3LHocZwSDNWH6VbeFOplUVEEQKHQtnmI6p04hHGdkiGvq1JJFx/Ln6vfTQuQmB7gdPMT2f9jrxwKW+d3/9vc/V/7Ei/jH4teg8qLDrhuimYJN09BEbWE3hB1D3ICoLUvVzC+6VUe6+XBWOtukIV1w3ECOMSXjU+nAEDVVEb6hhS1gBGcN5zOcQ0cKeyZQSe6IcKJ+ZsjRVovR1Ke0POForkxa0oRLKf6+LbFuZ0Iure4xTjzioUeU20xjF8/L6CwPGN1tks8mdFb7DKMSuSPBGbn3kNkSrCXMLwyY9CpkMwlmWihKByJhNoXtgRXKezSWQDt2qIhmMnGirEtOa1Y1WJHGXZ4SBB4/eeV9Ym2zUy5TaQUoBW/fO8P3js6Roqj7EeVKTGY0C6URW2GL5+Y2udZdJPWlqI6HJdKJQ5La6IEj/tkTh1yJJJ+uS+epI849vc0gKdPyAs6Vutwez9EPS6wsdTns1UEJdmksVYRDSIEKFnOSVoYzKCCZnk3WzNDTgqueK7ISWMshx0GFt4M15v0x65MZgjc7uGfHuK2IxIMckdlX7jnEHcHtcQ3llTH5ZkUORl/8xZVR0j3WZMnq9aTJsCJFMG8IO1B5uUTQ1lS2LBJfM7fYJ7lVJ64a6LmoTOE2Q2JjY4818XxK1sjQXoZzIEvspJ2S5jaZL12osWVyTavybyV1gx1oogshJJpcW2Ql8TbBgrQqI3nuQdLO8HYckoZAi1YojA1jw+CioTE/Zq41IkwdPnXuJt+4fZFuVuGJuT36iY/zToVwLiet5eQlQxC4VEoxwdCX5X0dKg8sane1+I6XDc5EMclcnru4wfZBR6IbP9Jn/WCGW8k8X3jrWfZVhfxOhaSTYk01Xteidk8xXTLkpZx6JWSUuWRGYzYq5GWxiIjnM+yBJVDH0MGeaD75gWtc122CebBCMXVrz40INmpYU03cznHGRVjGmQD2PLISpMsxlpvhVGKsHQ+rb1M9O+Tt9VWILB5d2+PJ6hYfW7zN672zRJ5CuzkZGmMUxlGny/24ZchKgFGkdUPUhumKL1z3WSn6xgalFOGMQC5xwxC3DcbWjM4ZjFZMV8wpghDN56hEk9bFDiGpioo+rsn0lRUwjrFhsqKZLsr3MI6QKDIX9l//yo83LKMyqD6QX7sDUfGVDhSN2wrvSAycjBI8TmCHwgxqpPG7gscn1ROhkpysWQkqO0JjPMkadIcyro7OCDY285YpxthclIuZQhnB/MR1kCJNSLrp4a0WFx/doVQPmXTLWFOBEnRcBPaen+B6idi0vt4ERCX36QvXWd+Z4fbmPOFCxlMr2wRfncMeyYkdt8QCIXcM4VyOnljsP2jLuBdrVKJxjoVJ0Xk/wz+UUT6tSjfvjBTJXELmSXiGcXPp5CoieoobOSU34ex8l+/unKVkJzh9i3G3zMFhnfbskGfObNKpTLlU2wdgudLnYnmPl5p3+dP1x7EqiSzWphq3GaHKIvipbmisY0c69lyRxhZ5NePo2iw3vniRfljijVcusRW1uFzf42zjmKYX4KxMyO2HoRxeTz5TlRcj6KaoSp2+RdrMsPt20THJUtwOYNwv0SwH/NLSa/zvG1e50DgiuTTl42fustwY4NzzxXUwsBmfS1ElMVSzD2W2PvleuQP+kTwKAsPJQRnOGbLCB6VxWwrv6KyitDIieDLAboeMQo+f/fnvQWRhvBxjG0pewvPP3EFnivI9B78WsTA7kH+nC56XQjU5DVgRC2uFM5CHWZhbOd5dH2tSpGYV93D9LviHcqC7Q6jesYnPhSgjJlpJ4Z45826CShT37s+xPWjQm5b4/F+8QKkS49spr2+uMnpQZ/xogtvXVB5YTI5LWJZ0zJ+8cgO7K9cpLRciq5mMdC4mbucsnOvy+r018Z9ZNCQ/kHjGuhdSmZ/QG5ZRKehAFltxKxeseSbi/EyXqhfhHdiokQ3nJlBNqN108LZd0loh1vMzcsfwnT9+mvb3HCobEsw9+7qiOy4XE7k0Ye6gaAJjm2RGbJ7tHZc8Uyy2hpK/OpNz670Vnr9wH3egmWQuu3GTVwaP8nNL77HUGhBPXKqtKRiZktKydOW6CBnxeuJ9Ywfq1EzP78rvdaIo7Yli1RkpnJGm9b5Av/6hMPfcnqa8p3CPNdZYF6HbioXvZZT2ZK+kE0VtA4x94hUltcjtaerr8v1PbM5/1OvHoriTQ/koJ2rIUvDEQz1sq1OzsNKBwR0K/iWkfglz0PFDvmdjXWiUVgjlXSnco7MFbUlEbKcPSVwXk3wx6jLosXRH1lhT2rEFpinUiGKzCVkj4/b2HOFYuoC0IiwLlQkebh5UCLerNNxQlrCJ/GBVK+KXn3pNHAtXBtwftKl+eo+sLIIcty//ztxrgn3mpZwnLz8QbvSRgz3SOBNFdVNx9KSFMoawLe8rtyGpGJSVY0XiBIkrcnQMIpYJFa6dcjSu8MLiA7QyJI0MUo2yDMfbTa4fztNwQzaDFvPlEcO4RJQ73Atm+cSZO5gDn3g2I68X2NHAodqZMl7LJVWqKrJ+hg7lDQfv/JDln3rA3nGdmafE7v/aYJG7vQ5h6pDerwq1b7tI1FGQW4bOtQyvqwmWM0rbFmk5R0Uatyedau7IlOaMDcSaIHG4G87xK+df4+39Zaz1Eu/3Frh5f0FYTk6OriRQymAiso60kZG+3aT0bJfKthyqSU2UoGnZELVzkqoYlhlX/hs3pLHgyohoo8Yza5v4JSEk/8mtJ6kvjACw6zGZUbz1nYtSDK+OeXF1g+NRRYKcJ4bJoITl5DgT8TJyhsIAkWWeXN7quiZckqzPtCQskZMmJisJ9zmYk8mVviOReVs2diAP/HjRxhkrKrdcput1Sm5C+/EjpmOPze0Of+vS2yxePETFmqgtEns1tUhCm/zI4+WN81jnxngHFmnFECzkeMcW1pFL85pmb6vNixfWC/aZIm5nrMz2+DuL3+e/fuqP+Oyl9wjnBRZ0BlLMwvmMvOcSZTb3t2dk31QI4xg5pBWYfSfD37fAMjj1mLyREixlDM8/5ISnZZgel8ld2Y3kJQnScUaQRRbWSDCNZC7BcjLJJ4gUuZujZyJef+NRnBFMU5ccxUcat/n28QUcneGUYyZbNUr7mrgpnPbyjsLriV1E1CnqSFpAh4H8PAINQdyU69+4IyyosKOoFtl0aUlqhUoFUvF66pQrf/iUTVIrIKWhiDO9ruLsv9oViufA4A1gsiwH2WTp4YL3h71+LIq7sWG0bFHbOvGKkAtW3TJ4fSnMaUU2xlGzsH5VcghMFwQnzR2xPz0R8ehELnhlqzDOKkHYQRZZRX5lMKcKuT7k9ZTcNpjVgHBOfEaC1QQrlO4uasmyZm1Rchj9C0Nh02SKrJKTlXI6Vw75+AvXWCn3CRcTdChMh3/5xgf5892L/OSlG0zfa/EbF77FNHLJZ2PCGUPYMWSeYf+FhwfVtQeLAjnVBKIwhZ2ofwSTZXlv3rE55cQqyxC1M0w5E751qnAGYoLkDhSj0OPq/DaDxOf9a2dQmaJ618Ze93EaEU/O7XLrcJaDaY037p/hwaDJvDPg7niWL777JMYx6FBgkXjiYk816dtNuT65Iu8LhcCUM6ZnE+I7dda/v4rjpvRem+NLbz/Jje0FlutDRolH+/GjU7MrO4DqtuCcB89ryntyyE5XZKSv35VF94nYY7pUuPRNLPrjEt89OsebwzP0D6vEC3JBWp0xn/nk63z0qZvksSWOkI50hDrUfOqzb9C/32TwCNQ2ThxHFfF8in+oxWZ3VpZ+OhOBSTiX470sCqF3NlcYb9WZjH2yvRLjew2xNjAKx8rQaxNZyu2XuNZdYL4xIitJjOOzF+6T9l2iVi6Ye90UviJCCTbLIcOnYplWUnXavdkTgXHiWnFPFyIYeyI5r9M1UVg6YxHURDM5wZUAa2lKEDuSFrbvYfkpf3TrKmFiU9qxMOUMdwT12xYm06h2xGOLB0THJcK1uFgUaqJORu7ljD8+4emLD/j+9y6R+eLhpDsRD3Y6/KNrP8NvfueX+fLdy7jHmrSVktSkIfP3pIu/uz1LqRaKayQy8VljSW7a/gRkT44hUyR9D+fQwZ2fYs4ERJ1cwj+A6syEpJZT2tfUb9oy9ViIA6VvpDGb2FRKMf/h5e8Qt3N0LSELbC5c2Wa6KOHjrxxe4I3xWc5WuhyMq2SphalkRC2DjhRRx+D3cpKyXNP2+xluv5gwC5U8xcLUiqTuNO4UOoSmIqlIslPzbi5GZIjwMmo//PxOvOG9Iqk686B5L8PYcPixBXFjdRQqFSvypCJ7qubt/EfW1R+L4q7yImXlgjAKxqsSODxdVARzgnXF9YK/XoiWTsZ5nQnfPW6IWVPmCxtmsqROi7n8fwqpuSyonKEhnJeuTCUKEk3WyFBbJVQizJnGew52IGKgrGTQI4uNB7P8u0+/QXC/dpra4u0Lr7335iyv3LvA9w/OYA9ssloGtYSXLt9l/+4M3946z/zze/yLrRf4+bPv4tz3Tt+/PS4UpbUMXUl46cI6admgA4V3YIlrXFOWb/6RksXzvDrF2/NE4/ZFMl/adKjfE0ZGddOQVAxJYlOzQ167dY7WmR7uscVkNZMD6k6Z790+zyOzRzze2uPp1S0en9nn5f5FPty5wycfv4HxZWy0ug5eNSIt55SfPyKZTVGRxppo8siCTGEf29jnx8w9s890v0K0nPDEo1tcXNqnG5Q5HFeYvjwrvOTC7mG8Ig+3jhT9S8KSqd21ZZE8EJjOIIXQLeCLvJzz0bV7/P3zX+C7985x+cIOJJq2P+EfPv7H/MlbV/n23QvCcfcycESIZi9O+fbOOa48dR+A/uNGWDRrU5wjm+mjMaPzGSqwwBWvkrwYrZMqZK2UdOieytX/zie+zQsv3MQ5tjH7HmHs8OkLhbHWRPPi/Aa+LY2CM4EfbC8J1DZU4mmeU6R5CYtLbfk033JJa5l4ADkiVVdGVLRWJBbI5T3hZKPkHj7pWKO27HByN8f0XLQ2TKceKtLolSlsl/jlx16nXQ449+l19MQimDWM13JIFNamz7s3Vnn68n1q77lUH+tR+8ChhHJMNdleGddKmX38EHuiOH7SwHYJ20uplULIFdHAl/dSZKmGc2Jw1lgZ0GxOmA59jDaUH9iQKoxjKO1D502N/U4VlahTJoz37Rqlt8u031XMvmmYLINShsrqCAwMH00JVjKCedkZGC33SONsn4oX88/f/5BYexx7lO+6rL+1jNtXRJnFC50NXts/Q5TbJJmFSeUZMpYhK4uosXdZnVpf7H5EEc5Jd50UsZaZL/Ul86XWTJaFqquMNAxuH2rrE9yROaVInjDcspLB78rv4yYkNUNjPefwaasIyDl5xoUSGXbk3wzbiv4jP7p8/1gUd8zDEUcn8kZOZL3OSDp2/0gWqGmZ0xzCUwhmX7p3d6hOvaDL+4b2dXkQSvvqlHpmbLEfSOryZ0/8a6p3bcr3HBG5xIIvThfkoJAwD0VezWjPDfnTjcf58IvvM70cFoeMwfRc6le7/N7z/4ewSeYjMAqTKSp2zKc+8B7BxGXr1hy/dfarvHJ4gbQmLBHp/A3W4hQ0uHdKhJk4CmYVwVFP7AZOfG6G52VcD2cLH/fAIqkanGOLuJEz+tAUv6sYPIJw369X+ZM3rnJxbY/BzTbRSow9ETZIWjE8trbL+1uL3Bt1GCceh0GVjzZv8fmtp/n6rYtUbrtMlzNyPycae5hqRu9BS1ggTn46QZALhJXfqDL86gK6nuCUE46mFW7tzHPYq2GMYnIuKby5BfvOXOi8awo8XT6b0YVUYueeNgWH+2FAgStiVw7DKr/17i/ymUvvc2t3Dq8dsNFv819e+7ex+hJMYQYuJtMQWRBpLswdsVgf8t7GUvGgKZyujb5dJl2OcPaLebcVo9RDq4e4YZieTWi95pyauNn3ff6XL3+Ea4cLOANF3koIA5c/eesqzaeP+Nmffo2jqMoTDRmvg1nD86ubOEN1ukTXSYGfGpmy0nZK/0pKadfGPbRwB7qwngXvSOOMJZs0qRUTaizMIp2oU3ETgI40xjHEh2XSoUttZYjzboWsk/Avrn2Qn1t4l81+E2sqmHHWSWTB18gpz0x5d3MJPtYj+1ab3nszTM8mlB7rM3fpkAV/yP69jc8FPgAAIABJREFUGfFQH2uySoZl5/y9c9/kDz76P/DkxU25L43shMpbokWJvt+m36+gtGQeR09OsaqJZP8uSWGMmwbjiRKbXP6/jqUTHp4Vn/U814S3G6TPjbCmmtKWGJUlUwdrqomahjB2cKyMTn1CeVdhnJzpakrWTvG78vUv3H+CT69cpxdLgLYx4O3auANNWhHI0Z4o/K7AM25PnyaFlQ6L5zERdos7hMqW4PQ6kXtVx1L0D5+tiuNrJs6zIraUZjSuy/fxj6B+TxAIq7CqMBbU1yV17SRO86TBVT+6cf/xKO4qkxMvqUJlRwpF0jC0bkjWYG4L1l7dFH8YKKwD6rLUmC4IS8aeyILFPxKZ7nRGM10Uf/b51xMquzm5KzRJlUqX6AwV9TsWUccI86S4IvZEn+JhUHh6jyyOdxt8cPEB375zAXvXo34X4sWE+UePOO5X+C++8Mvs3e9A38WaaOxDlz9//Qp/cesiH3v0DsYx/JMHn+ITc7eEG5uDPdK4PU26X8aux8SPBLzznUclNSiRBy93BVMs7Rsmy3Kz6xRqGxp7rLBHFl6vWL6lCnZ9JquCw9sTRTSbMb/aY+dLZyhdGOIcOCStjHgxQWWKG++vsjLbYxx73O+2aXgBL/cv8h+dfYWZzoi4bjCWjKpW10FZUtzUxMLpWyQNkd3rSoJKFdmFgMpP7pOPHazrFfY32mRTm0YtYNwtQyY4ZuaJKE08fzT2RK6Jd6yoPLDFYz+U3YiO5fMylnRNaMODQZNPn7nB19Yvko0d4sMyYewwmXpk9ZTffe7z2J0ANS7iGKsJ++MqPz33Pn4lFh+PvLDObeaUbvikZUPjloW942GmFt7xSXC6onrbIS0r/C1XmBNVQ1bLCW40ZWF638MUIeTj783y+bevcv1oni+tP46OJRR7fdim9dyhUCWL1wmNMa1A5a6Dv2sTrMXEM9kpRnsimglnpZDklinYPpx2q3FDvrb4HQkat8aWKGbrMZPbTdTzA5SVs9gZ8E/f+QlGg5JQFQECiYq0Rppgr8pL59cZb9cZnxcqrp5YDI8r7N3vsBM0KC2MJavUl2SwsOfzD975WX7rvV+UQyMQK41gJWNyJcI/kM9O73uYSD6PbGqjkP2WToUplM7GssPJ5L4t7YsbY9QS+i9FUcsdQ7ZRJavmBAs5cSdHOTlZOyFt5CSxza+f+Ra7+83Ch6aY0iNNMK+IEpufWr3J//zai9TsiLIXQ2QRzQhpwZpqyg/sInTeFN4wnPpYxXXB0e1QuvfJiikgs2JXVyhYw5mC3u2L0MwOhNJc2ZTnXidFAlvh95+WBao5Ud+fUL6tSGqcOxBo9q+Ft4zR0Lwpp9LwnD5lLAzPaREbHcrFCTti1p/5UN0UzHn27QyvR6E8hdGaYOnOSMZTnYB/DN3LDuNVoT3qwl7z5ASdLMmFdQay4Ta2dO9eT9gszlDhHwtjZ2ZpwGv/21P8ylPfp/ZEl2BO4VZjuv0qv3H1W/xnn/4Sy2ePRFAyE2OdHfPrH/sLFmYHvHLvAjMrfT4zf43Xe2tcfeYuzVsCyQSrKaaS4lyXDuLnf/JV4WiXM+KFhKQmfhdxs7hxagbvWKTJ7qDwby8Z4maxqJsq/H2N0YZwJqe2OMLWOc//rXeZbNfg3EQOgVSTz0U89vgmVTfi+dkHRBOXhhPyny98lT988CF8O5UuzM/J52LpZnY8Fh85xLg5SVMefmMbnHslwpUEdn2O35ijNDMlf2LMS0/f5sNP3KY3qLC8coxKFZM1iVMMW+IFlBRWEc5YFKpRyzBdMKe+PydOe0bLjY+CFxfvs+z1JAuzkmK1In7nypf5ly/+M371xe/w91//m7huhqlJsTBDF99J+e/+5DP8vcsvwyMT0UBUMmgkhE8ElHc1/Wdj3ItDsMTuNWll8hmcyQifnxAupKhjBytQ/DsvfZ+FZ/YIVxLChRTbS3nhU9cIHw1RocXV+W1KXlzkEMA49MiNdPlpXeCKE9gxc6WIhUsJ9Xdd7JFFsJQRtqXxSctGUo0yU+yDCp/7siGpGskOVbD7kiuQQSMl3KmQhjYXnt0kvlkvFDfwC5ff5sLKoRwaKTI17jvoBMqLY77/zcvoZow7O4WVADMjC+RffOH7fLx9m+lxmaiTkbZTvCMLpx7RqAaMJz6TqYcVKKy6jHQmFMO2cC1m9eoOKpbAeXIlnjWZHFD2VOFuiXAuL4kliMRPKkoHJ12xYjr2mL10RDqTUF8YybVIFX45lsjISPFrT36XPz1+kk9cukU4Y8TJ1TX4M4E8W0axPunw2x/+MufKR/h2wZhLFdNFeb5KB3J9dSxB3CovYF5XoLTWddEmZCXxCJLoS0EVspLAedVNddrA+kfqNEkrdzltKEsHiqhd4PapFPHyXk5cg9KxaAKiFgRzMDovP8dJo/vDXj8WPPd/8N/8/ucqH/uQGFwN1KmEPndl5NGZdCTt6xlxXRSpSV2Re4rU00Qd6WqMDemJ7adSWIVTnimgF0kxUURNg3EoYA45LKxIBEzBYkZeMlS2xQbh/yY5VjCOPJ77xE2+tn6JKHIxQ4fKypjfvvJVvt59jD++fpXEWMSRK6rS+z5/4+nX+cerr3NYyekmVTwnQymIMpt7TkM4vX5Oe2GIvRTwCxff4QvrV4iwsIYn4h2FFYtXTFKT8U28chRxS0K9McJ/jts57kCLejEv/HTmJygFdw5nsasJ9js1UVAmGmtgczCt8onzN3nj6AyT2GWUe6zUenyifYMfTFY5DCpSSIYOlDNMrgm3quR+YXHq5Thdm6SV4wwsWAlZvnjI0VGdbOow1g4br69y+YlNNo+b6EqKf9OXQORcCrkVS9HJfVEZ5p7B7QkkYWwpfihZgrsjRbSc0mlOeKe/wh8/+88ZVV3ujTtsph3+4M6HeWt7lUo1ZLJZo/zAIZkRrvy5MwfEzZxvvvI02dSR7rNscLpiK2ydnZL1PT506Q4P7s3jjhVWoPGf6NOZHzIclvHqEamlMCiuD+ZIkGQmU02p1iJuby2gNHz2mXf4yrtPyD0bOpT3FaOyzcLcgN6wgntgo4wSxlQG1W0YX0yxezbB2QQVa+H1+4a0okWIN1TF/S86jhN1MkahjVgZxHVF3Mnwdx2sSGNmY/o3O1Qv9wiPS+hyRi8ts/nqCklVxv+smeEc28TtnGTgUbowJNmpoHc8GDqotkQWJo7FF649zaXzu3TDMuW7Lo9/+hb/+PH/lb899yb/wZlX+c21N/g/S+cYjEo49Rgcg5na+Ds2h5SozE9QWz5pR5SheWyRVQqc2wF95IjVwkSz+tEtdnQFlYsVgNGK1ANdSmk3JwTfnSGaz6g+sJhWFGG3jGrHvPPuIzx94T5f/sEVjFJUfuARLcm+pHRgkS0kfHhpnWfL95l3BtwMF+nmPpnSqNjC2IUzqVvsfAo9RlFgiNuSCJYV0JoqogWtBOZeGxPMe3gDhd/NmSxJfans5Q/FVq5AaSiB/EoHcg8Ec/IsxA2FM4XBeY1VKM8lrEam2HAWjv/irwHPHQR7jRuFnNeWDk7H0LydUDqE0Rnx6D45HTFFVuJIinBaku7bO5bxr3VTaHtpIeG1IukOSgfyZ8r7Uiiqm4LXh7OG0o6Fv2OT/GVLTSWdclrNoZTx9p9eRiloNyZ4fUXVi/nd136OF1rr/OazX2e11cfraZxjm+R8yH/1g5/lxbd+iQ9W7nK1tcXGuMPWpMkk+b/aO/Noy666zn/2me658/Cm+6aqeq/mSqWqUhkqlUQMIQUJrSIaEbQFbXshiwYUEJXu1Yq2betaLAFplhOtNoi2gjTQMQZTScAkJpVKakwNr8ZXb773DXeezrT7j33qVUSCgkkKqu53rbvePfuc++7Zw/2d3/4N31+EzAkDaUoQkpViimo9yl8cvZVMvIU9b+InAvRwFyGFKtGlt1TWqZNW2XFSC0us9Qaqn21NFS1uqqxKAFMPWJMqEfga/tkE/q4a0lBaMSMt7t15kqVOAjdQJFnFpRRrzBV+/vGfJBdpqopNFyJIS4UgCh+Gds+rWF1Xw1g2Fdd4TVNhmDNRZg4PEU+3iV4ysS2X3a+Z4NRsXtnBL8Rp9yht049A5qya0/i8skNaJbWDkiEtrgiUk9WLSXpeUJ8RNYOmZ/HlzX/Dr8+/gWeX1tJYiVLt2HiujgwESbuDjKrix3pVRWtMFPsZTlZ4zd3HCeI+nd6AG2+Y4i33PQlC4szE+f5bTvL1Z7cp00BW0h5x6RzPUN2fJ3HUJriQgECQntChYhIcSysndltHAlrJJHHU5sGTN/Ku277OmzccxU+qKKzYJYNaJ4JWNpEbmviWxMnKcI1KYhdUJnPmsGI6jC1oihojLM3nxUInXlgCEFS0lzTUg3Zlm4Y0ILKo08kFBJsa9GTrRDZWaZzMImI+W3sL1DoRzK1VpR1HAE/gb2mgN1WuROt8CmlI1twxw/jtU/iuxj0bz/AbY1/i7Tc/zYViDzQM/F01Dj+/gbf+7Xv4kYM/x74n38utT7ybmTP9EAicSoRgxSKIKIKzWE+T5nwCswH2pEUk6hKkPawVHbOqEYShvMJTv+O5/aPoLS2sXKXYSa1lRe9bnOjDuK2E1tRUQZWOqhcrNPirH/k9jiyPsGlsARlRLJ84GnrGwY1LDMNnxCrxW+ffyKSjKpDlUqHtK5RHIrgil+zFKzIreyZQbK6xMIgjHeDklP8rdTFg8k0JOllJJw0r28PCOSlJbVRbNTU7YY1WN6UeDi+uQhYphfUONCXfzNoVGgLhKXlkL33rOPfvCs39v338Yx9J7b0dgdrWgNp2K4IcQGp0epTzM7CBkKzHi0n8WKhda8rj7sWVtq8+pzzboJx80gQp1CCLQIR8EcqxpexeIS+HGZptLOWo8pKS2LxiW0Rq/OC+ZxlMVemPNrgwO0DP2jLv2fI4Hzu8j4MHt9JMCFrSQEZAuhrvuOlpnjq3gYfP7uJso4c/vfGzSFNwcGktpZyGqBtkRiv89PZnaJsW+UyVjm9SXUkQxH2kVFERVk1TceyomGfNV4RjRlNt+cyGhptVBklpqK1ipwcIBPF8g/laim35BUSfS+lSBsLCIPqCSSEWYX16mXOlPprNCIYR0IravHXjszy1vJ6KY+PGJHpDaTR6U6PUjJEZK+MsRjGbYdJUoLQRe3OFsfUFFitJOkl47fgZRqJlelJN1qTLzOhxxIJFdDFM/c8pAiUnrT7vJlU/IyWlmUo9XAtCEGiaYuXL+RSX03ymfAMlJ0Y+XmOxleA/3/AQv7Ducd4+doC7e05zSK6hlQtwwx1S4GsULvUwWc9iLVgEJqwZWeTvn9zFm/c8z+s2neDzx29mbH2RlXocra1dCQPNSNwNbXxLOQvb+YAHbj9ItU+j6CaQSY+h3go3b5zkTLkf7IBctsHpap7lchIvIkjtXsYPNAZHSjRdiw3jCyyupAGorwN/rI2f8WhlBWZVhYGaDaXdB+ZlBlCVqehHw/WOhtESxOZUBqs0JHpLIDY00HWJ/Ics/ojDnTvPMF3NUmwm+eGxY1SDKJWpNH4yAENRGPueIim75eZzrBChfLiPYjPBv9t1nN3JS3zg4Fs4Xc6zNV+kJC3M55J4NvRtXqLZiuC1DWRLV2PmacQGGkTSHTpoIAXjw4s0dJ3YMYvqdg+vZWIVTARKkCYuCaSm1nIQ99GbOrF5QSerdqpWWaM96vLTO57hhNNPcy6BkALN1YitrVKvRQkcnWIsxb6+Uzz47M1gKLOiuWLgmWCWDaKjddYky/zkwDOcaI0w1cxhaQHLxTTkHLSqiZMOcPp8YnMarbxctd1rHaVZOzlJkPbQWjpBzMcu6tTGQwoRKbAa6r00lBy6TFPQ6VFyJlIOfU/2FR7+5JSkvlaEASBX6kvrbfXXrEFrKAA0Vh5/aeKw7wrh/t8/+rGP3Li8k+IdOqCEqOaqDntxVSgjtqDocAlpAuJzEj3kxBCojL7AEOROqfAhL64oClSYpXKWIpVZxmgpx4bU1fY/Nq/RGA0wmpqKKTcheVGjNRKEpo6QlrjXwywbnFwYohoxOTU3iO/rJHuaPHhuB/meCh/a+xBnOwOUW1HsSyZ+3mFFi/GebY/zvi2PkEy7/PKRH+aZqfWKe3o2ThAN8HXBCnGGolWeeWEj+b4KlUtp8DX0piCxpUxdM+g5otHJqkXiRdVWz00RbiElRl1DDnbQSqZyIAVKKCTGatwyMM2RwjCNf+zDG3EQutLC/T4XK+qy4sZYms6yZnQZF43b+ybJGE1+sPcoj1c2Yz8fx0krf0TQ7zI8ukJxNotdNHCyAZqj4Sd8pCZwmiaVc1kSIzXa9Qj3rjvF1xY3sT01x3Qriy80qtW4cqDFCEsbQmxBKI6XisDp9QmEAF0t6su87p6yEJEcr6BFfH53x+d5oP8Qn3z8fpL9dR6+dCOfm7yNz07cwf8t7KI6kSP5hE3gKYrb/Ppl3rd7P1oCzjeziJRLIt7h7i2neWR6C08e38a+nSc4X+ql3YwgMy52voljCfSagVE08WNShYBmHE6cW0PjQho/54GvUV5JUJQxvFKEH9xzmC2xAulIm4kj6/ByPu35OG0MXKHS3IUukYeS+DGIzWt40sCaNbEqGuk9i9R8iwANNKHMVbaqNytD7hovrehsNUej3adiyv2YJLoo6AiD0bElkpsqOL7BqUtDyKrJh25/GIng+eU11Fs2kUVd+VPaBnbBRAqYaaZhNkp0W5n+/ionFwe42OnjgfEjuIbOkZPriB2PIu4q8dodp1ibXCFiexCRbB2ZZ0nYRHMtOmfTDKwpUW1GwdcY6StRaseo2hbRGQO3z8ccauGlA5yIIL1nmVpSEHg6wte49c4JLibjeOi4gw5awyA+XuXpsxvYOFLESjs0ZxP4aV9l/V6KEmQ99o2e5lNf28cP7D3EuXPDEPPRetsYER9WTBKjdd47/Cj/c/51CAEnFvNUOzaOY6AtWfgxFUCgORrRgrJ3C6lKcOqOYo8FgdbSiSxrBIbSpDUvpA+IKSVSoNauH5OrpmYvoX6rnVxAYF7JzvZtVSBoNWIwJrFLgvraQNGXxxQtgeYopbT0yEubZYSU39oo/2ogOjgqR977fryE0jbcHg9zyQifdgqhDwirHNqUVxTHhOaKVR7wwJJIKyyQ4SibpFELn5aG8pQH0YD4pE5zSGm4Zl3ZW0WguL79hMqIFEFYeDcVYJaUY1cbavH6Dad5Z9/XOdkZ5PnGOv7m6dvYdeMFfnvtl6gFJr9y4Ue5q/c8nzm+B+lpiJKJzLqqaIKjEV1T45e2fRVdSD43t4fTx9aAkNyy+xzPXViLYfp89JbP81h1G185sYMda2dJmB2OFwfpT9Y5d2kAY9EMic0CIsu6ypYLS6tJDeobXERbR+8IvKSPtaJjbKkSBBo/tOE4g1aZT/zjPsxkB12XvHvbP/Cm5Ak+Wnwt+yc3016II6M+N2+a5PTiAM2ZBIObFjG1gEvnlDaqWT6ZdIO9+Us8/A834Wc8dNvHPBPljvuPsSd9gfVWgU/O3MvRk2t5022H0Ak4Uhqh4VgsTvRiFzXa/erh2swr3hcZ2jSlFSjqACNgaL/G3OvUNl1raUhdYtY0OoMukUwbp2ViWD5u3SLVV8cyfFbKccRCBGtdnVbZBkcDK8BcNOnZVaRwoZeh9YvU2hFapzOM3jLL7FMjfOgtXyRvlvnU9D3EDIcj0yMYhs+2/AKHT45hLeq4uYBIf5N23SKZbdKo2wRNQ60bX7B55xRn5vtV8lTD4I6bJlhsJThzfhC9qmh+41tLjGWXqbk2gRRcnOvFMH3cpsm920+hiYAes8FfP3YHxmiDiOVRr9mIYgTyHbRpGzflY1Z13H4XYQToRoBXttAcLSTSCpB2gLACBgbK9MUaPDDwHBm9yZyb5XB9DVHd5aGz2/DnYkhLkh6tYBo+Pzf+BEm9xd+t7ODrB27AzDf5rZu+RC2w+UpxJ0PRKptjCxypjfL4mU1IV1O8QjNR/H5Hkd3N22hDLT6wcz+/88z9CD2gv6/K7QOTPDK5mc5kUpkich5m0VTmLE8R9gVJDzyhKCNqit5ChIyNwoPRnfNMHx1UZsuUw9jAMpPPjeAPdshm6ziewRvXneTN6ef5+Pw+Dl5cS1A3WTte5EeHD/PJr7yRTXsnmZgbQAhJPlclaricL/QSLNrKaiBUFI8KhQ65n0rK7OglQj8HKuAiWhBUNwTYi0pg+/EAazmMIvOE+j/iSuF6EQgITY3CU+GkXkzJPyfrYzRUFI1vKe0eVD7I5citTk9A5qTgyB9+8Hkp5S3fTK6+IsJdCHEf8AnUxv/TUsrf/lbX28OjcvQ970cLuaj9iNr+KHpPqUqYwWpmqh+RaJ4gsqycD0IqIX05FllzLjtlJWZNaemX4+WloSZN6io+Xe8o7gerqiIyvJgSIlY1tIHZV7zgzh01OkVVZk84AjHQwTwbZeiuGS6cy9M3WuKmvhmOLg2zeLqXIKbCBd/7fft5cmU9x2aGEaBYEfub3Dt2hoee3qUIvoY6/MEdn6UtTf7riTdRnU+CJtmz/TzH5oe4f+wk/1gco3xgYLXk3OXsWalL8k97LO00Q+1d7Xou2w6FL3A2trh302n+/tCNYATY6Q4juTKm5nN6Ok8i1aLZsAlcbbUY940bZ7g5M8WyG+fBEzcimwZmRcft8bjnxlM8dngbZraNW7ZBk6BL6Cghiqchoh69vTV+at2zDJklJtqD1H1FpjbdynL4qU2r5FxuUgl3zVFREZGSKnvY7mWVI7sx6iufyIqktE0ioz7CCsgcsNj3zqe5OX6RX/76W/j0PX9CRmsRC2k23332bUwXc/htHdHWef/dD/O3he2cmR5AL0SIzQlu/4nD7H9qp1ovQy5vv/Vp9sbP8d5n34bv6MRSbXLxJnf1nydrNjhQGuPQ+bVsWrPAp9b/FcedPL999j4KUznefdej7I5O8r+Ld+IFOgcObuZtdz/F5w7twVg08QeVoXyov8yPjz7P+XYfXz68C9HSVwWYmjd43WuOkjLaDFoV/vjUnXQKKoxUcwSBHRC/pIeKDXR6fcVF4xLWCFAKjL22xts3PcsfH7uLyAtRtNvKvHfL14hrHcp+jD+7sJelmYyaPyHpG6xQrkXJJFu0H+tTIYih4hXfVmI4XeHE2RGMkqFisQ1Yd/MMPXaDpNHB1HwGrCqfefQ19G9dpFSLcd/6U0zWe2j7Bnt6JtlkL/CrD/0Y+mCT7UPznFzIAxAEAs8xoGJiVjXG9k4xuZRTOxwh8TydwNGxEx3et+1x/ujcXZSnMipL1BWkN61QmswqP5YVkMg2+eDW/fzGI29GZBxGB0pszhTY/9ROxECbn9vxBIHUuNTu4em5dVRrUcSCYloV/hXOIycjiSyLVYXTs8Ow1H5f+YeE0swvk92JQAl931aJUJGyyqL3LeVXqmy4ErfupiTWSujg01SkjBdT8y+CK9+tuSF3fJg4lZmQHPzzX3z1hLsQQgfOAPuAGeAg8DYp5cmX+ow9PCrXvOsDq4LUTYd248s/9kXFyqc5KkTSjypngtRfVC/SUE81FTalzl/mdL/szAgMxWURnVfx4LUNHnpdR+rqASFcJVja/YoOwGio7zCrKo4+ff88r82fYUdUkUU8tLKDJybHefPmY6y3i0w085yv97EzM8Oj85uZP9VPYAdoHY38liK3902yMVogZ9SZc7N8bWkzx6eHMC7a7LvvEA+d2E4mV+dn1j/DeKTA75y/n58YfZbz7X6+8PwtfN/2CZ44sYlbt1zk0DMbVfKIpRw6mqd2Hgg1Rno7DL8aCLCLGtqtZT5yw4PkjTKnO0Nc7PSxf24zlu5z3+BJ5joZxqOL/PXUbgpTOYyyzt2vPcbXzm9kfGCJ9aklxqKLfObsHuqFBPfffIwfzR3kQGMDf37mVjoziVVN52fue4wBs4KPxmMrWzj82Gb88Tb6RZuNd01y4uIQ1ozFmjtm0EXAxJlh7HljtT5lK69+HIGh+MqFoyKFCNSPxItCp8/H6GnjliP8+mu/yOdmb0cIybn5fjRdRfN4VQsz3cEvRDGrqtygtCS33XSWHclZNtvzfHZ+LydmB/n3NzzLTbFJAjQudPr5u8INaEjiZodThTz7xk7z9ZkN1KpRxJJFZE2dVk3VVq0tJIn2Nmk3LETJIjVWpu2YONNxNu6a5scGn+dMO89fH7wVra5jjjTwXIPb1k0yU88wkijz9JlxItMWUsBv/fjnALjg9PGXF26h3oywbbDAueVeIl9N0RwUBGHVMBXhpZyRCFbDiJVggso2H1Iun7zjLxjWK8z6aWbdHH9w9vsoLaQQtmIR1YuW0iQtyT13HmcsukRM7xBIjXYYMjZklbjY6WOunSFltEgYHepehKoX5YWVPPV2hEbVxpiL4MUk2fEV1meX2ZWa4dNfuxsyLlRNpO2zYazAuUsDaFWDD73+/+Gj8WcX9hI1XbZmFzi2PERhMc2a/AqpSJtqx6bUjGIZPpVaFITipDFsl3dtf5Kab3NgZR0TMwPIqkViuMpv3vBljrXWcLHZy2Mnt4Cjcc+uk/RG6nzhxE1sHi6wUEtSPZtl0+4pAik4Mz2AHXcYzlaYLaVpF+LYCzqJaUl5kxL27R656tMIIqqub319+GB1IHkJ6qPKlHulWLYqRNPJKuUzMSOpjyoF0k1K+g5LlnaFdnYBaDD0hM/sa8I8C01p67EFjXavJDarfIYTv/mBV1W47wU+IqV8Q3j8YQAp5f94qc/YQ6Ny6Bd/QWXYlQX2iqS8RRItaOoJ5qlF5yZVhSRQZFmRktLsLz8Z1XlBayAgsqSp7M+IEuqaD8JVTsbIirbKMdPpUXUNO7kgrJyiEmY6PSrG1iyryj/NwYD05hVaB3qJlNWOodOnvPbuiIMxb+ElAozeNn4hSs/GZWoH+3A2tAjqJtGXspyUAAALVUlEQVRZAycT4Gc8rHkTJ+czNL5EcSWFV7HIjZTZk59ioZXk2Mww+vkobibA7G/hNE32brrAhUoPhamcqiWZUjsaN6l8B0ZY6ADU1jbQ1QLRw9q0/bsKzM7lFK95zsGIeIz3L1Pp2CxM9iiHkxEgNIm2ZMFgG9P0Wd+3RMszuVTowTA9fE/HPBlD212h0zYRQuJWLaX1BSoFPjBC26QdMDq2yEiiTG+kTiA1XKkRSI2Ka/Pc+bVEzttKe0nK1UxcaQVYS7pydHcIE3lU/LG9qH5A7T6Jn+8QT6lQRwSY/S08RyXHmBEP/UiS1pY2WtFCb6uxsosh+6MB7UGP2JSh7P55B+lqEIBwNBKjVXriTcrNKI1mBKFJvLlYuHuUkPKQniCWbeF5Ok7JRmtpBNGAzGCV8koco2ip0Mq2wB/sqBqlF23iO1aoNyNkki3GM8ucKObpnE6v7ki9HhetYiB8weiuOcaTy7hS45nJMYwX4qtzvlo4XEi0jra6ZY8sQ3NY4gw54GmKUiDtEpQt9LqG1+uyYV2BdQlFZrLYTnD01FqVHKRBbrjMylwaLeahT9tYVUFjjeL58XMeesTHbxiqbkFBqbJOJkBaavdlLeo4vb5SnPJtdCPgzrELpM0WXqDjSo3lTpyj0yN4LQPR0InOK5rf9oCHWdWJby3ROprF29DCL1vYCwZmQylkkZ4W2WST5cP9ik2zDvVxn561JZodk+ZSDK2lq6S38Rq5RBNNSKYv9mFl24o/XgpGcmUCKRiI1nACnWMzw0gpCAKBfSKKm1akclIHra0c/4QMnsrHJJRpdFGnPeRhlnSlSCZUoqQXV+bDwAh9SCllczeaIizOLgmiUtGL6HKVZTJ7CuojKgjEXlZavhaGdUcLKvDAi6t8h4sffHU19weA+6SU/zE8/ilgj5TyPS/1mcjYiBx91wfwhjuk002SdoeRRJkLlR5y0SYdX4WONdoWo9kyE5OD3LThEg03QstTKcaXijn8tkEi22Q0UyZhdmh6Fg3XouFYq59dbMRpOyZCQLtlsXZgmWItQbMRIZVs0eqYGEZAMtqm2rRpNSLYMYf2fBwZ8zGiHol4m45r4HSUhy8e6xBIwWimzKmzw+RHV9ieWyBjNtkYLTDvZOg1a5xoDLPixGh6FtWOjRCSSsvGMnwMLWB2Joed7uD7Aq9jsHGkyK7cDF85dyO+p9GXrbFUSRCJuPQlGlwq9JBMqHvmVBK5pY5Tt5SjVJPKlthXwdQCmq7Jzt45al6EQ1OjDPVUcAONW/umeGp+HM/X2NpbYKUT40NrH+Z4e5QzzTwHC2voi9dZbCQYyyjStMlKjh9be4gb7RmKXpLTrSE0IZlvpyk5URpuhPlakqFUFV9q1B0LP9BYXEgzPLzCwnIa3fBJxdssLycQK5byhfQ3cF2dmO1g6AGJSIephdyVhbIUYfSGBSotG0MP+ODGRxg1lznQ3MCnT9/BtoEFvj93Fk0EFN0UD07dgK5J+uN1llsxBuNVmp7FTFnRMWtaQE+8yaX5Hvp7q9TbETKxFvOLaVKpFhHTo9q08X2NgUyNcssmYvjkok0arkVvtMHdvROcaeYZsKoMmBVOtwb56dxTPNMaZ8VLcKgyysRSP+tzy6xLLDNZ72FzqsDx8hC5SJOZeobFWpxMvMXchV60lMvGoSJnToxgDTRxHQPpaQzlS8xO9SAiqgSdpgUMp6ost2IsV+MAxKMdKtU4ftUkNVijP1lnvpJiOF2hx24wU8+QsVsst2IkTIek1WamlsEPNBKRDi3XJB+v0fAsdBGoso5CUqwl0IREAr6v0ZdsUGpG2dpXwBABTqATN1SC01OTY+SzNRxfJxnpsDW9wP7JzXRaJsP9ZeaW0/Rm6hhaQNx0mKum+ODW/XyxsBtL85hvpFbvrebYVJ0IpVoMTZPYlku5HOd1mycAmKpnCRB0PIOY6XB2rp+b1k6zKVHk8YWNZO0WlY6N4xns6J3jxEqeuOUQ0T3mqynW55ZYaiWYmuth05oFJi4MKtNgxCcW79CYSiEkRNfUaBTiaC2N3KYVRlMlis0ki5UEpumzqbdI1mpxuDjMtt4CEc3jyUvj5FIN+mINzhT6cNomG4eLtDyTzZkiR5eGMHWfrN2i5ZlMLvQgBKRTDWoNm2A2hp9zEVUTva+NBFW8fD5GkPQQZoBheZz/8V/97hPuQoh3Au8MD7cDL7ysN/K9h15g6WrfxFVEt//Xd/+hOwbfSf/XSin7vtkJ45s1/hsxC4y+6HgkbPsnkFL+EfBHAEKI517q6XO94Hofg27/r+/+Q3cMXu7+vxIZqgeBjUKIMSGEBbwV+Mor8D1ddNFFF128BF52zV1K6Qkh3gN8FRUK+SdSyhMv9/d00UUXXXTx0nglzDJIKR8CHvo2PvJNM6yuM1zvY9DtfxfX+xi8rP3/rshQ7aKLLrro4uXFdwUrZBdddNFFFy8vusK9iy666OIaxFUX7kKI+4QQE0KIc0KIX7na9/NKQAgxKoR4XAhxUghxQgjx82F7TgjxiBDibPg3G7YLIcTvhWNyTAix++r24OWBEEIXQhwWQjwYHo8JIQ6E/fyrMLoKIUQkPD4Xnl93Ne/75YIQIiOE+IIQ4rQQ4pQQYu/1tAaEEO8P1/8LQoi/FELY1/IaEEL8iRCiKIR44UVt3/Z8CyHeEV5/Vgjxjn/t919V4R7y0HwKuB/YBrxNCLHtat7TKwQP+KCUchtwO/Cfwn7+CvColHIj8Gh4DGo8NoavdwK//+rf8iuCnwdOvej4d4CPSSk3ACXgZ8P2nwVKYfvHwuuuBXwCeFhKuQXYiRqL62INCCGGgfcBt0gpt6Mi6d7Ktb0G/gy47xvavq35FkLkgF8D9gC3Ab92+YHwL0JKedVewF7gqy86/jDw4at5T69Sv7+MIlabAAbDtkFgInz/hyiytcvXr173vfpCJbM9CtwDPIgiTF0CjG9cC6gw2r3heyO8TlztPvwb+58GLn5jP66XNQAMA9NALpzTB4E3XOtrAFgHvPCdzjfwNuAPX9T+T677Vq+rbZa5POGXMRO2XbMIt5c3AQeAASnlfHhqARgI31+L4/Jx4JdYrV1PD1CWUnrh8Yv7uNr/8HwlvP57GWPAIvCnoWnq00KIONfJGpBSzgIfBaaAedScPs/1tQbg25/v73gdXG3hfl1BCJEA/gb4BSll9cXnpHosX5NxqUKIHwCKUsrnr/a9XEUYwG7g96WUNwENrmzJgWt+DWSBN6EeckNAnH9usriu8ErP99UW7v8qHpprAUIIEyXYPyel/GLYXBBCDIbnB4Fi2H6tjcudwA8JISaB/4MyzXwCyAghLifSvbiPq/0Pz6eB5Vfzhl8BzAAzUsoD4fEXUML+elkD9wIXpZSLUkoX+CJqXVxPawC+/fn+jtfB1Rbu1wUPjRBCAP8LOCWl/N0XnfoKcNn7/Q6ULf5y+9tDD/rtQOVFW7nvOUgpPyylHJFSrkPN8WNSyp8EHgceCC/7xv5fHpcHwuu/pzVaKeUCMC2E2Bw2vQ44yXWyBlDmmNuFELHw93C5/9fNGgjx7c73V4HXCyGy4e7n9WHbv4zvAofDG1GVm84D/+Vq388r1Me7UNuvY8CR8PVGlA3xUeAssB/IhdcLVBTReeA4KsLgqvfjZRqLu4EHw/fjwLPAOeDzQCRst8Pjc+H58at93y9T33cBz4Xr4EtA9npaA8CvA6dR9N6fBSLX8hoA/hLlX3BRO7ef/U7mG/gP4TicA37mX/v9XfqBLrroootrEFfbLNNFF1100cUrgK5w76KLLrq4BtEV7l100UUX1yC6wr2LLrro4hpEV7h30UUXXVyD6Ar3LrroootrEF3h3kUXXXRxDeL/A86+2P2TkkIPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(fbank.T, origin = 'lower')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_vQWPblSTFOw"
   },
   "source": [
    "#### 由于声学模型网络结构原因（3个maxpooling层），我们的音频数据的每个维度需要能够被8整除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O3D9NlljTFOx"
   },
   "outputs": [],
   "source": [
    "fbank = fbank[:fbank.shape[0]//8*8, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "Md4gYnFrTFOy",
    "outputId": "ac0dfa1f-39f7-47fc-b397-a24a9e6b2207"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1040, 200)\n"
     ]
    }
   ],
   "source": [
    "print(fbank.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w0LQ11PkTFO1"
   },
   "source": [
    "#### 总结：\n",
    "- 对音频数据进行时频转换\n",
    "- 转换后的数据需要各个维度能够被8整除\n",
    "\n",
    "### 2.4 数据生成器\n",
    "#### 确定batch_size和batch_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tUmMcmhATFO1"
   },
   "outputs": [],
   "source": [
    "total_nums = 10000\n",
    "batch_size = 4\n",
    "batch_num = total_nums // batch_size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qUxHfxe4TFO4"
   },
   "source": [
    "#### shuffle\n",
    "打乱数据的顺序，我们通过查询乱序的索引值，来确定训练数据的顺序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ejqsLFJdTFO4"
   },
   "outputs": [],
   "source": [
    "from random import shuffle\n",
    "shuffle_list = [i for i in range(10000)]\n",
    "shuffle(shuffle_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YD4qWOq0TFO6"
   },
   "source": [
    "#### generator\n",
    "batch_size的信号时频图和标签数据，存放到两个list中去"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ccsv7q1rTFO7"
   },
   "outputs": [],
   "source": [
    "def get_batch(batch_size, shuffle_list, wav_lst, label_data, vocab):\n",
    "    for i in range(10000//batch_size):\n",
    "        wav_data_lst = []\n",
    "        label_data_lst = []\n",
    "        begin = i * batch_size\n",
    "        end = begin + batch_size\n",
    "        sub_list = shuffle_list[begin:end]\n",
    "        for index in sub_list:\n",
    "            fbank = compute_fbank(wav_lst[index])\n",
    "            fbank = fbank[:fbank.shape[0] // 8 * 8, :]\n",
    "            label = word2id(label_data[index], vocab)\n",
    "            wav_data_lst.append(fbank)\n",
    "            label_data_lst.append(label)\n",
    "        yield wav_data_lst, label_data_lst\n",
    "\n",
    "batch = get_batch(4, shuffle_list, wav_lst, label_data, vocab)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 161
    },
    "colab_type": "code",
    "id": "gNfYVbEhTFO8",
    "outputId": "f640a09c-0b19-437e-e3b7-a0a6c02d116e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(912, 200)\n",
      "(784, 200)\n",
      "(800, 200)\n",
      "(896, 200)\n",
      "[221, 274, 824, 158, 837, 48, 264, 962, 1172, 48, 264, 274, 408, 146, 351, 76, 301, 1147, 274, 125, 339, 31, 297, 324, 22, 336, 491, 624, 753, 18, 234, 408]\n",
      "[648, 625, 54, 560, 346, 977, 374, 280, 108, 430, 405, 281, 84, 186, 90, 37, 250, 53, 445, 53, 286, 37, 250, 208, 271, 290, 86, 346, 19, 353, 97, 282]\n",
      "[977, 290, 146, 267, 20, 507, 502, 109, 137, 180, 16, 174, 137, 180, 20, 489, 246, 346, 174, 800, 66, 243, 104, 721, 7, 755, 254, 209, 94, 97, 499, 1049, 66, 443, 505, 230]\n",
      "[41, 720, 496, 203, 618, 96, 324, 576, 687, 342, 41, 22, 430, 28, 324, 203, 41, 22, 417, 28, 496, 203, 41, 22, 704, 28]\n"
     ]
    }
   ],
   "source": [
    "wav_data_lst, label_data_lst = next(batch)\n",
    "for wav_data in wav_data_lst:\n",
    "    print(wav_data.shape)\n",
    "for label_data in label_data_lst:\n",
    "    print(label_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "id": "EtpmJxsaTFO-",
    "outputId": "2439a771-8413-4746-f4ed-d3824b5bc7d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "912\n",
      "[912, 784, 800, 896]\n"
     ]
    }
   ],
   "source": [
    "lens = [len(wav) for wav in wav_data_lst]\n",
    "print(max(lens))\n",
    "print(lens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QRcBRBo_TFPA"
   },
   "source": [
    "#### padding\n",
    "然而，每一个batch_size内的数据有一个要求，就是需要构成成一个tensorflow块，这就要求每个样本数据形式是一样的。\n",
    "除此之外，ctc需要获得的信息还有输入序列的长度。\n",
    "这里输入序列经过卷积网络后，长度缩短了8倍，因此我们训练实际输入的数据为wav_len//8。\n",
    "- padding wav data\n",
    "- wav len // 8 （网络结构导致的）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "id": "4lKAfKnKTFPB",
    "outputId": "718126fd-198b-4d9e-efd4-74acaf0190a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 912, 200, 1)\n",
      "[114  98 100 112]\n"
     ]
    }
   ],
   "source": [
    "def wav_padding(wav_data_lst):\n",
    "    wav_lens = [len(data) for data in wav_data_lst]\n",
    "    wav_max_len = max(wav_lens)\n",
    "    wav_lens = np.array([leng//8 for leng in wav_lens])\n",
    "    new_wav_data_lst = np.zeros((len(wav_data_lst), wav_max_len, 200, 1))\n",
    "    for i in range(len(wav_data_lst)):\n",
    "        new_wav_data_lst[i, :wav_data_lst[i].shape[0], :, 0] = wav_data_lst[i]\n",
    "    return new_wav_data_lst, wav_lens\n",
    "\n",
    "pad_wav_data_lst, wav_lens = wav_padding(wav_data_lst)\n",
    "print(pad_wav_data_lst.shape)\n",
    "print(wav_lens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ttw65iZvTFPD"
   },
   "source": [
    "同样也要对label进行padding和长度获取，不同的是数据维度不同，且label的长度就是输入给ctc的长度，不需要额外处理\n",
    "- label padding\n",
    "- label len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 53
    },
    "colab_type": "code",
    "id": "sW1HcU5PTFPD",
    "outputId": "c9f8219e-f71c-4620-cf3e-33144d6b3d8b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 36)\n",
      "[32 32 36 26]\n"
     ]
    }
   ],
   "source": [
    "def label_padding(label_data_lst):\n",
    "    label_lens = np.array([len(label) for label in label_data_lst])\n",
    "    max_label_len = max(label_lens)\n",
    "    new_label_data_lst = np.zeros((len(label_data_lst), max_label_len))\n",
    "    for i in range(len(label_data_lst)):\n",
    "        new_label_data_lst[i][:len(label_data_lst[i])] = label_data_lst[i]\n",
    "    return new_label_data_lst, label_lens\n",
    "\n",
    "pad_label_data_lst, label_lens = label_padding(label_data_lst)\n",
    "print(pad_label_data_lst.shape)\n",
    "print(label_lens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QzhiqU4sTFPG"
   },
   "source": [
    "- 用于训练格式的数据生成器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CjLXnThATFPG"
   },
   "outputs": [],
   "source": [
    "def data_generator(batch_size, shuffle_list, wav_lst, label_data, vocab):\n",
    "    for i in range(len(wav_lst)//batch_size):\n",
    "        wav_data_lst = []\n",
    "        label_data_lst = []\n",
    "        begin = i * batch_size\n",
    "        end = begin + batch_size\n",
    "        sub_list = shuffle_list[begin:end]\n",
    "        for index in sub_list:\n",
    "            fbank = compute_fbank(wav_lst[index])\n",
    "#             print(wav_lst[index])\n",
    "            pad_fbank = np.zeros((fbank.shape[0]//8*8+8, fbank.shape[1]))\n",
    "            pad_fbank[:fbank.shape[0], :] = fbank\n",
    "            label = word2id(label_data[index], vocab)\n",
    "            wav_data_lst.append(pad_fbank)\n",
    "            label_data_lst.append(label)\n",
    "#             break\n",
    "        pad_wav_data, input_length = wav_padding(wav_data_lst)\n",
    "        pad_label_data, label_length = label_padding(label_data_lst)\n",
    "        inputs = {'the_inputs': pad_wav_data,\n",
    "                  'the_labels': pad_label_data,\n",
    "                  'input_length': input_length,\n",
    "                  'label_length': label_length,\n",
    "                 }\n",
    "#         print(pad_wav_data,pad_label_data,input_length,label_length)\n",
    "        outputs = {'ctc': np.zeros(pad_wav_data.shape[0],)} \n",
    "        yield inputs, outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-A0wB8VoTFPI"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5MKnT0MRTFPJ"
   },
   "source": [
    "## 3. 模型搭建\n",
    "\n",
    "训练输入为时频图，标签为对应的拼音标签，如下所示：\n",
    "\n",
    "\n",
    "搭建语音识别模型，采用了 CNN+CTC 的结构。\n",
    "![dfcnn.jpg](attachment:dfcnn.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "YzxubHEHTFPK",
    "outputId": "27ad0557-19e7-4514-d506-bfc933b656ee"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.layers import Input, Conv2D, BatchNormalization, MaxPooling2D\n",
    "from keras.layers import Reshape, Dense, Lambda\n",
    "from keras.optimizers import Adam\n",
    "from keras import backend as K\n",
    "from keras.models import Model\n",
    "from keras.utils import multi_gpu_model\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Cn02BtPCTFPL"
   },
   "source": [
    "- 定义3*3的卷积层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kQsJljWTTFPN"
   },
   "outputs": [],
   "source": [
    "def conv2d(size):\n",
    "    return Conv2D(size, (3,3), use_bias=True, activation='relu',\n",
    "        padding='same', kernel_initializer='he_normal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vy8NBYcSTFPR"
   },
   "source": [
    "- 定义batch norm层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N3LroAuTTFPR"
   },
   "outputs": [],
   "source": [
    "def norm(x):\n",
    "    return BatchNormalization(axis=-1)(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hhOGw1CETFPU"
   },
   "source": [
    "- 定义最大池化层，数据的后两维维度都减半"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-ZU5qMysTFPV"
   },
   "outputs": [],
   "source": [
    "def maxpool(x):\n",
    "    return MaxPooling2D(pool_size=(2,2), strides=None, padding=\"valid\")(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Osh8shMYTFPY"
   },
   "source": [
    "- dense层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cZrqYemFTFPY"
   },
   "outputs": [],
   "source": [
    "def dense(units, activation=\"relu\"):\n",
    "    return Dense(units, activation=activation, use_bias=True,\n",
    "        kernel_initializer='he_normal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qIIIQ8tiTFPa"
   },
   "source": [
    "- 由cnn + cnn + maxpool构成的组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3m_eAg-ETFPb"
   },
   "outputs": [],
   "source": [
    "# x.shape=(none, none, none)\n",
    "# output.shape = (1/2, 1/2, 1/2)\n",
    "def cnn_cell(size, x, pool=True):\n",
    "    x = norm(conv2d(size)(x))\n",
    "    x = norm(conv2d(size)(x))\n",
    "    if pool:\n",
    "        x = maxpool(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jhtoya1WTFPd"
   },
   "source": [
    "- **添加CTC损失函数，由backend引入**\n",
    "\n",
    "**注意：CTC_batch_cost输入为：**\n",
    "\n",
    "- **labels** 标签：[batch_size, l]\n",
    "- **y_pred** cnn网络的输出：[batch_size, t, vocab_size]\n",
    "- **input_length** 网络输出的长度：[batch_size]\n",
    "- **label_length** 标签的长度：[batch_size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YX8s5naeTFPd"
   },
   "outputs": [],
   "source": [
    "def ctc_lambda(args):\n",
    "    labels, y_pred, input_length, label_length = args\n",
    "    y_pred = y_pred[:, :, :]\n",
    "    return K.ctc_batch_cost(labels, y_pred, input_length, label_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rDl6EDj2TFPf"
   },
   "source": [
    "### **搭建cnn+dnn+ctc的声学模型**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DBF5GrQsTFPg"
   },
   "outputs": [],
   "source": [
    "class Amodel():\n",
    "    \"\"\"docstring for Amodel.\"\"\"\n",
    "    def __init__(self, vocab_size):\n",
    "        super(Amodel, self).__init__()\n",
    "        self.vocab_size = vocab_size\n",
    "        self._model_init()\n",
    "        self._ctc_init()\n",
    "        self.opt_init()\n",
    "\n",
    "    def _model_init(self):\n",
    "        self.inputs = Input(name='the_inputs', shape=(None, 200, 1))\n",
    "        self.h1 = cnn_cell(32, self.inputs)\n",
    "        self.h2 = cnn_cell(64, self.h1)\n",
    "        self.h3 = cnn_cell(128, self.h2)\n",
    "        self.h4 = cnn_cell(128, self.h3, pool=False)\n",
    "        # 200 / 8 * 128 = 3200\n",
    "        self.h6 = Reshape((-1, 3200))(self.h4)\n",
    "        self.h7 = dense(256)(self.h6)\n",
    "        self.outputs = dense(self.vocab_size, activation='softmax')(self.h7)\n",
    "        self.model = Model(inputs=self.inputs, outputs=self.outputs)\n",
    "\n",
    "    def _ctc_init(self):\n",
    "        self.labels = Input(name='the_labels', shape=[None], dtype='float32')\n",
    "        self.input_length = Input(name='input_length', shape=[1], dtype='int64')\n",
    "        self.label_length = Input(name='label_length', shape=[1], dtype='int64')\n",
    "        self.loss_out = Lambda(ctc_lambda, output_shape=(1,), name='ctc')\\\n",
    "            ([self.labels, self.outputs, self.input_length, self.label_length])\n",
    "        self.ctc_model = Model(inputs=[self.labels, self.inputs,\n",
    "            self.input_length, self.label_length], outputs=self.loss_out)\n",
    "\n",
    "    def opt_init(self):\n",
    "        opt = Adam(lr = 0.0008, beta_1 = 0.9, beta_2 = 0.999, decay = 0.01, epsilon = 10e-8)\n",
    "        #self.ctc_model=multi_gpu_model(self.ctc_model,gpus=2)\n",
    "        self.ctc_model.compile(loss={'ctc': lambda y_true, output: output}, optimizer=opt)\n",
    "#     def load_model(self,file):\n",
    "#         self.ctc_model.load_model(file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1169
    },
    "colab_type": "code",
    "id": "KEm3xnI_TFPh",
    "outputId": "e15f6e0c-6a2a-4e01-b50e-2734095010cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/zero/anaconda/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From /Users/zero/anaconda/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4249: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "WARNING:tensorflow:From /Users/zero/anaconda/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4229: to_int64 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "the_inputs (InputLayer)         (None, None, 200, 1) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, None, 200, 32 320         the_inputs[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, None, 200, 32 128         conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, None, 200, 32 9248        batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, None, 200, 32 128         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, None, 100, 32 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, None, 100, 64 18496       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, None, 100, 64 256         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, None, 100, 64 36928       batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, None, 100, 64 256         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, None, 50, 64) 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, None, 50, 128 73856       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, None, 50, 128 512         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, None, 50, 128 147584      batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, None, 50, 128 512         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, None, 25, 128 0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, None, 25, 128 147584      max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, None, 25, 128 512         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, None, 25, 128 147584      batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, None, 25, 128 512         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "reshape_1 (Reshape)             (None, None, 3200)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, None, 256)    819456      reshape_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "the_labels (InputLayer)         (None, None)         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, None, 1176)   302232      dense_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "input_length (InputLayer)       (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "label_length (InputLayer)       (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "ctc (Lambda)                    (None, 1)            0           the_labels[0][0]                 \n",
      "                                                                 dense_2[0][0]                    \n",
      "                                                                 input_length[0][0]               \n",
      "                                                                 label_length[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 1,706,104\n",
      "Trainable params: 1,704,696\n",
      "Non-trainable params: 1,408\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "am = Amodel(1176)\n",
    "am.ctc_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1RfnYMzxTFPk"
   },
   "source": [
    "## 4. 开始训练\n",
    "\n",
    "这样训练所需的数据，就准备完毕了，接下来可以进行训练了。我们采用如下参数训练：\n",
    "- batch_size = 4\n",
    "- batch_num = 10000 // 4\n",
    "- epochs = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LabqR5zwTFPn"
   },
   "source": [
    "- **准备训练数据，shuffle是为了打乱训练数据顺序**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s7zbpGYxTFPk"
   },
   "outputs": [],
   "source": [
    "total_nums = 100\n",
    "batch_size = 20\n",
    "batch_num = total_nums // batch_size\n",
    "epochs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "Owm5b_LNTFPn",
    "outputId": "da2dea9f-8294-4e92-8d6b-b7e5bc995921"
   },
   "outputs": [],
   "source": [
    "source_file = 'data_thchs30'\n",
    "label_lst, wav_lst = source_get(source_file)\n",
    "label_data = gen_label_data(label_lst[:100])\n",
    "vocab = mk_vocab(label_data)\n",
    "vocab_size = len(vocab)\n",
    "\n",
    "# print(vocab_size)\n",
    "\n",
    "shuffle_list = [i for i in range(100)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Rg60YIxITFPp"
   },
   "source": [
    "- 使用fit_generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ps_FNHSkTFPp"
   },
   "source": [
    "- 开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2717
    },
    "colab_type": "code",
    "id": "Ont2NPn1TFPq",
    "outputId": "423532ad-9029-460d-9821-b9e96001f22d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is the 1 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 117s 23s/step - loss: 347.9154\n",
      "this is the 2 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 104s 21s/step - loss: 235.1735\n",
      "this is the 3 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 225.2550\n",
      "this is the 4 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 216.4999\n",
      "this is the 5 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 206.7374\n",
      "this is the 6 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 200.8864\n",
      "this is the 7 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 195.0210\n",
      "this is the 8 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 188.3000\n",
      "this is the 9 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 180.1939\n",
      "this is the 10 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 169.9448\n",
      "this is the 11 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 104s 21s/step - loss: 157.3016\n",
      "this is the 12 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 141.5696\n",
      "this is the 13 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 105s 21s/step - loss: 123.5799\n",
      "this is the 14 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 104.5176\n",
      "this is the 15 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 104s 21s/step - loss: 86.4973\n",
      "this is the 16 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 69.8939\n",
      "this is the 17 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 52.5046\n",
      "this is the 18 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 36.3740\n",
      "this is the 19 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 105s 21s/step - loss: 23.3699\n",
      "this is the 20 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 14.2179\n",
      "this is the 21 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 8.7206\n",
      "this is the 22 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 104s 21s/step - loss: 5.3433\n",
      "this is the 23 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 3.4416\n",
      "this is the 24 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 2.4633\n",
      "this is the 25 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 105s 21s/step - loss: 1.8703\n",
      "this is the 26 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 1.5321\n",
      "this is the 27 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 1.3031\n",
      "this is the 28 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 104s 21s/step - loss: 1.1399\n",
      "this is the 29 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 1.0234\n",
      "this is the 30 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 105s 21s/step - loss: 0.9349\n",
      "this is the 31 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 0.8605\n",
      "this is the 32 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 0.8003\n",
      "this is the 33 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 105s 21s/step - loss: 0.7504\n",
      "this is the 34 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 0.7101\n",
      "this is the 35 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 0.6733\n",
      "this is the 36 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 106s 21s/step - loss: 0.6400\n",
      "this is the 37 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 104s 21s/step - loss: 0.6108\n",
      "this is the 38 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 104s 21s/step - loss: 0.5856\n",
      "this is the 39 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 0.5622\n",
      "this is the 40 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 0.5404\n",
      "this is the 41 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 0.5216\n",
      "this is the 42 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 0.5031\n",
      "this is the 43 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 0.4859\n",
      "this is the 44 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 0.4702\n",
      "this is the 45 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 0.4562\n",
      "this is the 46 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 0.4416\n",
      "this is the 47 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 0.4305\n",
      "this is the 48 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 0.4179\n",
      "this is the 49 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 102s 20s/step - loss: 0.4054\n",
      "this is the 50 th epochs trainning !!!\n",
      "Epoch 1/1\n",
      "5/5 [==============================] - 103s 21s/step - loss: 0.3947\n"
     ]
    }
   ],
   "source": [
    "am = Amodel(vocab_size)\n",
    "\n",
    "for k in range(epochs):\n",
    "    print('this is the', k+1, 'th epochs trainning !!!')\n",
    "    #shuffle(shuffle_list)\n",
    "    batch = data_generator(batch_size, shuffle_list, wav_lst, label_data, vocab)\n",
    "    am.ctc_model.fit_generator(batch, steps_per_epoch=batch_num, epochs=1)\n",
    "am.ctc_model.save_weights('res2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "with open('model_def.json','w') as ff:\n",
    "    json_string = am.model.to_json()\n",
    "    ff.write(json_string)#保存模型信息\n",
    "    \n",
    "sep = '\\n'\n",
    "fl=open('pinyin_list.txt', 'w')\n",
    "fl.write(sep.join(py))\n",
    "fl.close()#保存拼音列表"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "CNN+CTC_tutorial.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
